From bd1dbd5228191b80426e017999a874ee0c406ea3 Mon Sep 17 00:00:00 2001 From: ChongWei905 Date: Tue, 3 Sep 2024 19:03:25 +0800 Subject: [PATCH 1/4] docs: add requirements and renew forms for readmes --- README.md | 14 +- benchmark_results.md | 199 ++++++++++++++-------------- configs/README.md | 6 +- configs/bit/README.md | 17 ++- configs/cmt/README.md | 17 ++- configs/coat/README.md | 17 ++- configs/convit/README.md | 23 ++-- configs/convnext/README.md | 23 ++-- configs/convnextv2/README.md | 23 ++-- configs/crossvit/README.md | 23 ++-- configs/densenet/README.md | 23 ++-- configs/dpn/README.md | 17 ++- configs/edgenext/README.md | 23 ++-- configs/efficientnet/README.md | 23 ++-- configs/ghostnet/README.md | 17 ++- configs/googlenet/README.md | 23 ++-- configs/halonet/README.md | 17 ++- configs/hrnet/README.md | 23 ++-- configs/inceptionv3/README.md | 23 ++-- configs/inceptionv4/README.md | 23 ++-- configs/mixnet/README.md | 23 ++-- configs/mnasnet/README.md | 23 ++-- configs/mobilenetv1/README.md | 23 ++-- configs/mobilenetv2/README.md | 23 ++-- configs/mobilenetv3/README.md | 27 ++-- configs/mobilevit/README.md | 23 ++-- configs/nasnet/README.md | 23 ++-- configs/pit/README.md | 23 ++-- configs/poolformer/README.md | 24 ++-- configs/pvt/README.md | 23 ++-- configs/pvtv2/README.md | 23 ++-- configs/regnet/README.md | 23 ++-- configs/repmlp/README.md | 17 ++- configs/repvgg/README.md | 27 ++-- configs/res2net/README.md | 23 ++-- configs/resnest/README.md | 17 ++- configs/resnet/README.md | 23 ++-- configs/resnetv2/README.md | 23 ++-- configs/resnext/README.md | 23 ++-- configs/rexnet/README.md | 23 ++-- configs/senet/README.md | 23 ++-- configs/shufflenetv1/README.md | 23 ++-- configs/shufflenetv2/README.md | 23 ++-- configs/sknet/README.md | 23 ++-- configs/squeezenet/README.md | 23 ++-- configs/swintransformer/README.md | 23 ++-- configs/swintransformerv2/README.md | 23 ++-- configs/vgg/README.md | 27 ++-- configs/visformer/README.md | 23 ++-- configs/vit/README.md | 11 +- configs/volo/README.md | 13 +- configs/xception/README.md | 17 ++- configs/xcit/README.md | 23 ++-- 53 files changed, 779 insertions(+), 527 deletions(-) diff --git a/README.md b/README.md index 84dbd600f..4ad48a801 100644 --- a/README.md +++ b/README.md @@ -29,13 +29,13 @@ MindCV is an open-source toolbox for computer vision research and development ba The following is the corresponding `mindcv` versions and supported `mindspore` versions. -| mindcv | mindspore | -|:------:|:----------:| -| main | master | -| v0.4.0 | 2.3.0 | -| 0.3.0 | 2.2.10 | -| 0.2 | 2.0 | -| 0.1 | 1.8 | +| mindcv | mindspore | +| :----: | :---------: | +| main | master | +| v0.4.0 | 2.3.0/2.3.1 | +| 0.3.0 | 2.2.10 | +| 0.2 | 2.0 | +| 0.1 | 1.8 | ### Major Features diff --git a/benchmark_results.md b/benchmark_results.md index 276d707b1..325b3445e 100644 --- a/benchmark_results.md +++ b/benchmark_results.md @@ -3,61 +3,60 @@ performance tested on Ascend 910(8p) with graph mode -| model | top-1 (%) | top-5 (%) | params(M) | batch size | cards | ms/step | jit_level | recipe | download | -| ---------------------- | --------- | --------- | --------- | ---------- | ----- | ------- | --------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- | -| bit_resnet50 | 76.81 | 93.17 | 25.55 | 32 | 8 | 74.52 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/bit/BiT_resnet50-1e4795a4.ckpt) | -| cmt_small | 83.24 | 96.41 | 26.09 | 128 | 8 | 500.64 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/cmt/cmt_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/cmt/cmt_small-6858ee22.ckpt) | -| coat_tiny | 79.67 | 94.88 | 5.50 | 32 | 8 | 207.74 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_tiny-071cb792.ckpt) | -| convit_tiny | 73.66 | 91.72 | 5.71 | 256 | 8 | 231.62 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_tiny-e31023f2.ckpt) | -| convnext_tiny | 81.91 | 95.79 | 28.59 | 16 | 8 | 66.79 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt) | -| convnextv2_tiny | 82.43 | 95.98 | 28.64 | 128 | 8 | 400.20 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) | -| crossvit_9 | 73.56 | 91.79 | 8.55 | 256 | 8 | 550.79 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/crossvit/crossvit_9-e74c8e18.ckpt) | -| densenet121 | 75.64 | 92.84 | 8.06 | 32 | 8 | 43.28 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet121-120_5004_Ascend.ckpt) | -| dpn92 | 79.46 | 94.49 | 37.79 | 32 | 8 | 78.22 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn92_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn92-e3e0fca.ckpt) | -| edgenext_xx_small | 71.02 | 89.99 | 1.33 | 256 | 8 | 191.24 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_xx_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/edgenext/edgenext_xx_small-afc971fb.ckpt) | -| efficientnet_b0 | 76.89 | 93.16 | 5.33 | 128 | 8 | 172.78 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/efficientnet/efficientnet_b0-103ec70c.ckpt) | -| ghostnet_050 | 66.03 | 86.64 | 2.60 | 128 | 8 | 211.13 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_050_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_050-85b91860.ckpt) | -| googlenet | 72.68 | 90.89 | 6.99 | 32 | 8 | 21.40 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/googlenet/googlenet-5552fcd3.ckpt) | -| halonet_50t | 79.53 | 94.79 | 22.79 | 64 | 8 | 421.66 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/halonet/halonet_50t_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/halonet/halonet_50t-533da6be.ckpt) | -| hrnet_w32 | 80.64 | 95.44 | 41.30 | 128 | 8 | 279.10 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/hrnet/hrnet_w32_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/hrnet/hrnet_w32-cc4fbd91.ckpt) | -| inception_v3 | 79.11 | 94.40 | 27.20 | 32 | 8 | 76.42 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v3/inception_v3-38f67890.ckpt) | -| inception_v4 | 80.88 | 95.34 | 42.74 | 32 | 8 | 76.19 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v4/inception_v4-db9c45b3.ckpt) | -| mixnet_s | 75.52 | 92.52 | 4.17 | 128 | 8 | 252.49 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mixnet/mixnet_s-2a5ef3a3.ckpt) | -| mnasnet_075 | 71.81 | 90.53 | 3.20 | 256 | 8 | 165.43 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mnasnet/mnasnet_0.75_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mnasnet/mnasnet_075-465d366d.ckpt) | -| mobilenet_v1_025 | 53.87 | 77.66 | 0.47 | 64 | 8 | 42.43 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv1/mobilenet_v1_025-d3377fba.ckpt) | -| mobilenet_v2_075 | 69.98 | 89.32 | 2.66 | 256 | 8 | 155.94 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_075-bd7bd4c4.ckpt) | -| mobilenet_v3_small_100 | 68.10 | 87.86 | 2.55 | 75 | 8 | 48.14 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-509c6047.ckpt) | -| mobilenet_v3_large_100 | 75.23 | 92.31 | 5.51 | 75 | 8 | 47.49 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-1279ad5f.ckpt) | -| mobilevit_xx_small | 68.91 | 88.91 | 1.27 | 64 | 8 | 53.52 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilevit/mobilevit_xx_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilevit/mobilevit_xx_small-af9da8a0.ckpt) | -| nasnet_a_4x1056 | 73.65 | 91.25 | 5.33 | 256 | 8 | 330.89 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/nasnet/nasnet_a_4x1056_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/nasnet/nasnet_a_4x1056-0fbb5cdd.ckpt) | -| pit_ti | 72.96 | 91.33 | 4.85 | 128 | 8 | 271.50 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pit/pit_ti_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pit/pit_ti-e647a593.ckpt) | -| poolformer_s12 | 77.33 | 93.34 | 11.92 | 128 | 8 | 220.13 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/poolformer/poolformer_s12_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/poolformer/poolformer_s12-5be5c4e4.ckpt) | -| pvt_tiny | 74.81 | 92.18 | 13.23 | 128 | 8 | 229.63 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvt/pvt_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pvt/pvt_tiny-6abb953d.ckpt) | -| pvt_v2_b0 | 71.50 | 90.60 | 3.67 | 128 | 8 | 269.38 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvtv2/pvt_v2_b0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pvt_v2/pvt_v2_b0-1c4f6683.ckpt) | -| regnet_x_800mf | 76.04 | 92.97 | 7.26 | 64 | 8 | 42.49 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/regnet/regnet_x_800mf_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/regnet/regnet_x_800mf-617227f4.ckpt) | -| repmlp_t224 | 76.71 | 93.30 | 38.30 | 128 | 8 | 578.23 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repmlp/repmlp_t224_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repmlp/repmlp_t224-8dbedd00.ckpt) | -| repvgg_a0 | 72.19 | 90.75 | 9.13 | 32 | 8 | 20.58 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a0-6e71139d.ckpt) | -| repvgg_a1 | 74.19 | 91.89 | 14.12 | 32 | 8 | 20.70 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a1-539513ac.ckpt) | -| res2net50 | 79.35 | 94.64 | 25.76 | 32 | 8 | 39.68 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/res2net/res2net_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/res2net/res2net50-f42cf71b.ckpt) | -| resnest50 | 80.81 | 95.16 | 27.55 | 128 | 8 | 244.92 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnest/resnest50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnest/resnest50-f2e7fc9c.ckpt) | -| resnet50 | 76.69 | 93.50 | 25.61 | 32 | 8 | 31.41 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnet/resnet_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnet/resnet50-e0733ab8.ckpt) | -| resnetv2_50 | 76.90 | 93.37 | 25.60 | 32 | 8 | 32.66 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnetv2/resnetv2_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnetv2/resnetv2_50-3c2f143b.ckpt) | -| resnext50_32x4d | 78.53 | 94.10 | 25.10 | 32 | 8 | 37.22 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnext/resnext50_32x4d_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnext/resnext50_32x4d-af8aba16.ckpt) | -| rexnet_09 | 77.06 | 93.41 | 4.13 | 64 | 8 | 130.10 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/rexnet/rexnet_x09_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/rexnet/rexnet_09-da498331.ckpt) | -| seresnet18 | 71.81 | 90.49 | 11.80 | 64 | 8 | 44.40 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/senet/seresnet18_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/senet/seresnet18-7880643b.ckpt) | -| shufflenet_v1_g3_05 | 57.05 | 79.73 | 0.73 | 64 | 8 | 40.62 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/shufflenet/shufflenetv1/shufflenet_v1_g3_05-42cfe109.ckpt) | -| shufflenet_v2_x0_5 | 60.53 | 82.11 | 1.37 | 64 | 8 | 41.87 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/shufflenet/shufflenetv2/shufflenet_v2_x0_5-8c841061.ckpt) | -| skresnet18 | 73.09 | 91.20 | 11.97 | 64 | 8 | 45.84 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/sknet/skresnet18_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/sknet/skresnet18-868228e5.ckpt) | -| squeezenet1_0 | 59.01 | 81.01 | 1.25 | 32 | 8 | 22.36 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/squeezenet/squeezenet_1.0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/squeezenet/squeezenet1_0-e2d78c4a.ckpt) | -| swin_tiny | 80.82 | 94.80 | 33.38 | 256 | 8 | 454.49 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformer/swin_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/swin/swin_tiny-0ff2f96d.ckpt) | -| swinv2_tiny_window8 | 81.42 | 95.43 | 28.78 | 128 | 8 | 317.19 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/swinv2/swinv2_tiny_window8-3ef8b787.ckpt) | -| vgg13 | 72.87 | 91.02 | 133.04 | 32 | 8 | 55.20 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg13-da805e6e.ckpt) | -| vgg19 | 75.21 | 92.56 | 143.66 | 32 | 8 | 67.42 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg19-bedee7b6.ckpt) | -| visformer_tiny | 78.28 | 94.15 | 10.33 | 128 | 8 | 217.92 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/visformer/visformer_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/visformer/visformer_tiny-daee0322.ckpt) | -| vit_b_32_224 | 75.86 | 92.08 | 87.46 | 512 | 8 | 454.57 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vit/vit_b32_224_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vit/vit_b_32_224-7553218f.ckpt) | -| volo_d1 | 82.59 | 95.99 | 27 | 128 | 8 | 270.79 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/volo/volo_d1_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/volo/volo_d1-c7efada9.ckpt) | -| xception | 79.01 | 94.25 | 22.91 | 32 | 8 | 92.78 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/xception/xception_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/xception/xception-2c1e711df.ckpt) | -| xcit_tiny_12_p16_224 | 77.67 | 93.79 | 7.00 | 128 | 8 | 252.98 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/xcit/xcit_tiny_12_p16_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/xcit/xcit_tiny_12_p16_224-1b1c9301.ckpt) | +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- | +| bit_resnet50 | 25.55 | 8 | 32 | 224x224 | O2 | 146s | 74.52 | 3413.33 | 76.81 | 93.17 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/bit/BiT_resnet50-1e4795a4.ckpt) | +| cmt_small | 26.09 | 8 | 128 | 224x224 | O2 | 1268s | 500.64 | 2048.01 | 83.24 | 96.41 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/cmt/cmt_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/cmt/cmt_small-6858ee22.ckpt) | +| coat_tiny | 5.50 | 8 | 32 | 224x224 | O2 | 543s | 254.95 | 1003.92 | 79.67 | 94.88 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_tiny-071cb792.ckpt) | +| convit_tiny | 5.71 | 8 | 256 | 224x224 | O2 | 133s | 231.62 | 8827.59 | 73.66 | 91.72 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_tiny-e31023f2.ckpt) | +| convnext_tiny | 28.59 | 8 | 16 | 224x224 | O2 | 127s | 66.79 | 1910.45 | 81.91 | 95.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt) | +| convnextv2_tiny | 28.64 | 8 | 128 | 224x224 | O2 | 237s | 400.20 | 2560.00 | 82.43 | 95.98 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) | +| crossvit_9 | 8.55 | 8 | 256 | 240x240 | O2 | 206s | 550.79 | 3719.30 | 73.56 | 91.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/crossvit/crossvit_9-e74c8e18.ckpt) | +| densenet121 | 8.06 | 8 | 32 | 224x224 | O2 | 191s | 43.28 | 5914.97 | 75.64 | 92.84 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet121-120_5004_Ascend.ckpt) | +| dpn92 | 37.79 | 8 | 32 | 224x224 | O2 | 293s | 78.22 | 3272.82 | 79.46 | 94.49 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn92_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn92-e3e0fca.ckpt) | +| dpn92 | 37.79 | 8 | 32 | 224x224 | O2 | 293s | 78.22 | 3272.82 | 79.46 | 94.49 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn92_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn92-e3e0fca.ckpt) | +| efficientnet_b0 | 5.33 | 8 | 128 | 224x224 | O2 | 203s | 172.78 | 5926.61 | 76.89 | 93.16 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/efficientnet/efficientnet_b0-103ec70c.ckpt) | +| ghostnet_050 | 2.60 | 8 | 128 | 224x224 | O2 | 383s | 211.13 | 4850.09 | 66.03 | 86.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_050_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_050-85b91860.ckpt) | +| googlenet | 6.99 | 8 | 32 | 224x224 | O2 | 72s | 21.40 | 11962.62 | 72.68 | 90.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/googlenet/googlenet-5552fcd3.ckpt) | +| halonet_50t | 22.79 | 8 | 64 | 256x256 | O2 | 261s | 421.66 | 6437.82 | 79.53 | 94.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/halonet/halonet_50t_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/halonet/halonet_50t-533da6be.ckpt) | +| hrnet_w32 | 41.30 | 128 | 8 | 224x224 | O2 | 1312s | 279.10 | 3668.94 | 80.64 | 95.44 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/hrnet/hrnet_w32_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/hrnet/hrnet_w32-cc4fbd91.ckpt) | +| inception_v3 | 27.20 | 8 | 32 | 299x299 | O2 | 120s | 76.42 | 3349.91 | 79.11 | 94.40 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v3/inception_v3-38f67890.ckpt) | +| inception_v4 | 42.74 | 8 | 32 | 299x299 | O2 | 177s | 76.19 | 3360.02 | 80.88 | 95.34 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v4/inception_v4-db9c45b3.ckpt) | +| mixnet_s | 4.17 | 8 | 128 | 224x224 | O2 | 556s | 252.49 | 4055.61 | 75.52 | 92.52 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mixnet/mixnet_s-2a5ef3a3.ckpt) | +| mnasnet_075 | 3.20 | 8 | 256 | 224x224 | O2 | 140s | 165.43 | 12379.86 | 71.81 | 90.53 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mnasnet/mnasnet_0.75_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mnasnet/mnasnet_075-465d366d.ckpt) | +| mobilenet_v1_025 | 0.47 | 8 | 64 | 224x224 | O2 | 89s | 42.43 | 12066.93 | 53.87 | 77.66 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv1/mobilenet_v1_025-d3377fba.ckpt) | +| mobilenet_v2_075 | 2.66 | 8 | 256 | 224x224 | O2 | 164s | 155.94 | 13133.26 | 69.98 | 89.32 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_075-bd7bd4c4.ckpt) | +| mobilenet_v3_small_100 | 2.55 | 8 | 75 | 224x224 | O2 | 145s | 48.14 | 12463.65 | 68.10 | 87.86 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-509c6047.ckpt) | +| mobilenet_v3_large_100 | 5.51 | 8 | 75 | 224x224 | O2 | 271s | 47.49 | 12634.24 | 75.23 | 92.31 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-1279ad5f.ckpt) | +| mobilevit_xx_small | 1.27 | 64 | 8 | 256x256 | O2 | 301s | 53.52 | 9566.52 | 68.91 | 88.91 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilevit/mobilevit_xx_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilevit/mobilevit_xx_small-af9da8a0.ckpt) | +| nasnet_a_4x1056 | 5.33 | 8 | 256 | 224x224 | O2 | 656s | 330.89 | 6189.37 | 73.65 | 91.25 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/nasnet/nasnet_a_4x1056_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/nasnet/nasnet_a_4x1056-0fbb5cdd.ckpt) | +| pit_ti | 4.85 | 8 | 128 | 224x224 | O2 | 192s | 271.50 | 3771.64 | 72.96 | 91.33 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pit/pit_ti_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pit/pit_ti-e647a593.ckpt) | +| poolformer_s12 | 11.92 | 8 | 128 | 224x224 | O2 | 118s | 220.13 | 4651.80 | 77.33 | 93.34 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/poolformer/poolformer_s12_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/poolformer/poolformer_s12-5be5c4e4.ckpt) | +| pvt_tiny | 13.23 | 8 | 128 | 224x224 | O2 | 192s | 229.63 | 4459.35 | 74.81 | 92.18 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvt/pvt_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pvt/pvt_tiny-6abb953d.ckpt) | +| pvt_v2_b0 | 3.67 | 8 | 128 | 224x224 | O2 | 269s | 269.38 | 3801.32 | 71.50 | 90.60 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvtv2/pvt_v2_b0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pvt_v2/pvt_v2_b0-1c4f6683.ckpt) | +| regnet_x_800mf | 7.26 | 8 | 64 | 224x224 | O2 | 99s | 42.49 | 12049.89 | 76.04 | 92.97 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/regnet/regnet_x_800mf_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/regnet/regnet_x_800mf-617227f4.ckpt) | +| repmlp_t224 | 38.30 | 8 | 128 | 224x224 | O2 | 289s | 578.23 | 1770.92 | 76.71 | 93.30 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repmlp/repmlp_t224_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repmlp/repmlp_t224-8dbedd00.ckpt) | +| repvgg_a0 | 9.13 | 8 | 32 | 224x224 | O2 | 50s
| 20.58 | 12439.26 | 72.19 | 90.75 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a0-6e71139d.ckpt) | +| repvgg_a1 | 14.12 | 8 | 32 | 224x224 | O2 | 29s | 20.70 | 12367.15 | 74.19 | 91.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a1-539513ac.ckpt) | +| res2net50 | 25.76 | 8 | 32 | 224x224 | O2 | 119s | 39.68 | 6451.61 | 79.35 | 94.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/res2net/res2net_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/res2net/res2net50-f42cf71b.ckpt) | +| resnest50 | 27.55 | 8 | 128 | 224x224 | O2 | 83s | 244.92 | 4552.73 | 80.81 | 95.16 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnest/resnest50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnest/resnest50-f2e7fc9c.ckpt) | +| resnet50 | 25.61 | 8 | 32 | 224x224 | O2 | 43s | 31.41 | 8150.27 | 76.69 | 93.50 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnet/resnet_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnet/resnet50-e0733ab8.ckpt) | +| resnetv2_50 | 25.60 | 8 | 32 | 224x224 | O2 | 52s | 32.66 | 7838.33 | 76.90 | 93.37 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnetv2/resnetv2_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnetv2/resnetv2_50-3c2f143b.ckpt) | +| resnext50_32x4d | 25.10 | 8 | 32 | 224x224 | O2 | 49s | 37.22 | 6878.02 | 78.53 | 94.10 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnext/resnext50_32x4d_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnext/resnext50_32x4d-af8aba16.ckpt) | +| rexnet_09 | 4.13 | 8 | 64 | 224x224 | O2 | 462s | 130.10 | 3935.43 | 77.06 | 93.41 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/rexnet/rexnet_x09_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/rexnet/rexnet_09-da498331.ckpt) | +| seresnet18 | 11.80 | 8 | 64 | 224x224 | O2 | 43s | 44.40 | 11531.53 | 71.81 | 90.49 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/senet/seresnet18_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/senet/seresnet18-7880643b.ckpt) | +| shufflenet_v1_g3_05 | 0.73 | 8 | 64 | 224x224 | O2 | 169s | 40.62 | 12604.63 | 57.05 | 79.73 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/shufflenet/shufflenetv1/shufflenet_v1_g3_05-42cfe109.ckpt) | +| shufflenet_v2_x0_5 | 1.37 | 8 | 64 | 224x224 | O2 | 62s | 41.87 | 12228.33 | 60.53 | 82.11 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/shufflenet/shufflenetv2/shufflenet_v2_x0_5-8c841061.ckpt) | +| skresnet18 | 11.97 | 8 | 64 | 224x224 | O2 | 60s | 45.84 | 11169.28 | 73.09 | 91.20 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/sknet/skresnet18_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/sknet/skresnet18-868228e5.ckpt) | +| squeezenet1_0 | 1.25 | 8 | 32 | 224x224 | O2 | 45s | 22.36 | 11449.02 | 58.67 | 80.61 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/squeezenet/squeezenet_1.0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/squeezenet/squeezenet1_0-eb911778.ckpt) | +| swin_tiny | 33.38 | 8 | 256 | 224x224 | O2 | 226s | 454.49 | 4506.15 | 80.82 | 94.80 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformer/swin_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/swin/swin_tiny-0ff2f96d.ckpt) | +| swinv2_tiny_window8 | 28.78 | 8 | 128 | 256x256 | O2 | 273s | 317.19 | 3228.35 | 81.42 | 95.43 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/swinv2/swinv2_tiny_window8-3ef8b787.ckpt) | +| vgg13 | 133.04 | 8 | 32 | 224x224 | O2 | 23s | 55.20 | 4637.68 | 72.87 | 91.02 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg13-da805e6e.ckpt) | +| vgg19 | 143.66 | 8 | 32 | 224x224 | O2 | 22s | 67.42 | 3797.09 | 75.21 | 92.56 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg19-bedee7b6.ckpt) | +| visformer_tiny | 10.33 | 8 | 128 | 224x224 | O2 | 137s | 217.92 | 4698.97 | 78.28 | 94.15 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/visformer/visformer_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/visformer/visformer_tiny-daee0322.ckpt) | +| volo_d1 | 27 | 8 | 128 | 224x224 | O2 | 275s | 270.79 | 3781.53 | 82.59 | 95.99 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/visformer/visformer_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/visformer/visformer_tiny-df995ba4-910v2.ckpt) | +| xception | 22.91 | 8 | 32 | 299x299 | O2 | 161s | 96.78 | 2645.17 | 79.01 | 94.25 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/xception/xception_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/xception/xception-2c1e711df.ckpt) | +| xcit_tiny_12_p16_224 | 7.00 | 8 | 128 | 224x224 | O2 | 382s | 252.98 | 4047.75 | 77.67 | 93.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/xcit/xcit_tiny_12_p16_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/xcit/xcit_tiny_12_p16_224-1b1c9301.ckpt) | @@ -66,50 +65,52 @@ -| model | top-1 (%) | top-5 (%) | params(M) | batch size | cards | ms/step | jit_level | recipe | download | -| ---------------------- | --------- | --------- | --------- | ---------- | ----- | ------- | --------- | ------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- | -| convit_tiny | 73.79 | 91.70 | 5.71 | 256 | 8 | 226.51 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-1961717e-910v2.ckpt) | -| convnext_tiny | 81.28 | 95.61 | 28.59 | 16 | 8 | 48.7 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnext/convnext_tiny-db11dc82-910v2.ckpt) | -| convnextv2_tiny | 82.39 | 95.95 | 28.64 | 128 | 8 | 257.2 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-a35b79ce-910v2.ckpt) | -| crossvit_9 | 73.38 | 91.51 | 8.55 | 256 | 8 | 514.36 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/crossvit/crossvit_9-32c69c96-910v2.ckpt) | -| densenet121 | 75.67 | 92.77 | 8.06 | 32 | 8 | 47.34 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/densenet/densenet121-bf4ab27f-910v2.ckpt) | -| edgenext_xx_small | 70.64 | 89.75 | 1.33 | 256 | 8 | 239.38 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/edgenext/edgenext_xx_small-cad13d2c-910v2.ckpt) | -| efficientnet_b0 | 76.88 | 93.28 | 5.33 | 128 | 8 | 172.64 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/efficientnet/efficientnet_b0-f8d7aa2a-910v2.ckpt) | -| googlenet | 72.89 | 90.89 | 6.99 | 32 | 8 | 23.5 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/googlenet/googlenet-de74c31d-910v2.ckpt) | -| hrnet_w32 | 80.66 | 95.30 | 41.30 | 128 | 8 | 238.03 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/hrnet/hrnet_w32_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/hrnet/hrnet_w32-e616cdcb-910v2.ckpt) | -| inception_v3 | 79.25 | 94.47 | 27.20 | 32 | 8 | 70.83 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v3/inception_v3-61a8e9ed-910v2.ckpt) | -| inception_v4 | 80.98 | 95.25 | 42.74 | 32 | 8 | 80.97 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v4/inception_v4-56e798fc-910v2.ckpt) | -| mixnet_s | 75.58 | 95.54 | 4.17 | 128 | 8 | 228.03 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mixnet/mixnet_s-fe4fcc63-910v2.ckpt) | -| mnasnet_075 | 71.77 | 90.52 | 3.20 | 256 | 8 | 175.85 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mnasnet/mnasnet_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mnasnet/mnasnet_075-083b2bc4-910v2.ckpt) | -| mobilenet_v1_025 | 54.05 | 77.74 | 0.47 | 64 | 8 | 47.47 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv1/mobilenet_v1_025-cbe3d3b3-910v2.ckpt) | -| mobilenet_v2_075 | 69.73 | 89.35 | 2.66 | 256 | 8 | 174.65 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_075-755932c4-910v2.ckpt) | -| mobilenet_v3_small_100 | 68.07 | 87.77 | 2.55 | 75 | 8 | 52.38 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-6fa3c17d-910v2.ckpt) | -| mobilenet_v3_large_100 | 75.59 | 92.57 | 5.51 | 75 | 8 | 55.89 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-bd4e7bdc-910v2.ckpt) | -| mobilevit_xx_small | 67.11 | 87.85 | 1.27 | 64 | 8 | 67.24 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilevit/mobilevit_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilevit/mobilevit_xx_small-6f2745c3-910v2.ckpt) | -| nasnet_a_4x1056 | 74.12 | 91.36 | 5.33 | 256 | 8 | 364.35 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/nasnet/nasnet_a_4x1056_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/nasnet/nasnet_a_4x1056-015ba575c-910v2.ckpt) | -| pit_ti | 73.26 | 91.57 | 4.85 | 128 | 8 | 266.47 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pit/pit_ti_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pit/pit_ti-33466a0d-910v2.ckpt) | -| poolformer_s12 | 77.49 | 93.55 | 11.92 | 128 | 8 | 211.81 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/poolformer/poolformer_s12_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/poolformer/poolformer_s12-c7e14eea-910v2.ckpt) | -| pvt_tiny | 74.88 | 92.12 | 13.23 | 128 | 8 | 237.5 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvt/pvt_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt/pvt_tiny-6676051f-910v2.ckpt) | -| pvt_v2_b0 | 71.25 | 90.50 | 3.67 | 128 | 8 | 255.76 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvtv2/pvt_v2_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt_v2/pvt_v2_b0-d9cd9d6a-910v2.ckpt) | -| regnet_x_800mf | 76.11 | 93.00 | 7.26 | 64 | 8 | 50.74 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/regnet/regnet_x_800mf_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/regnet/regnet_x_800mf-68fe1cca-910v2.ckpt) | -| repvgg_a0 | 72.29 | 90.78 | 9.13 | 32 | 8 | 24.12 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a0-b67a9f15-910v2.ckpt) | -| repvgg_a1 | 73.68 | 91.51 | 14.12 | 32 | 8 | 28.29 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a1-a40aa623-910v2.ckpt) | -| res2net50 | 79.33 | 94.64 | 25.76 | 32 | 8 | 39.6 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/res2net/res2net_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/res2net/res2net50-aa758355-910v2.ckpt) | -| resnet50 | 76.76 | 93.31 | 25.61 | 32 | 8 | 31.9 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnet/resnet_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnet/resnet50-f369a08d-910v2.ckpt) | -| resnetv2_50 | 77.03 | 93.29 | 25.60 | 32 | 8 | 32.19 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnetv2/resnetv2_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnetv2/resnetv2_50-a0b9f7f8-910v2.ckpt) | -| resnext50_32x4d | 78.64 | 94.18 | 25.10 | 32 | 8 | 44.61 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnext/resnext50_32x4d_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnext/resnext50_32x4d-988f75bc-910v2.ckpt) | -| rexnet_09 | 76.14 | 92.96 | 4.13 | 64 | 8 | 115.61 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/rexnet/rexnet_x09_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/rexnet/rexnet_09-00223eb4-910v2.ckpt) | -| seresnet18 | 72.05 | 90.59 | 11.80 | 64 | 8 | 51.09 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/senet/seresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/senet/seresnet18-7b971c78-910v2.ckpt) | -| shufflenet_v1_g3_05 | 57.08 | 79.89 | 0.73 | 64 | 8 | 47.77 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv1/shufflenet_v1_g3_05-56209ef3-910v2.ckpt) | -| shufflenet_v2_x0_5 | 60.65 | 82.26 | 1.37 | 64 | 8 | 47.32 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv2/shufflenet_v2_x0_5-39d05bb6-910v2.ckpt) | -| skresnet18 | 72.85 | 90.83 | 11.97 | 64 | 8 | 49.83 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/sknet/skresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/sknet/skresnet18-9d8b1afc-910v2.ckpt) | -| squeezenet1_0 | 58.75 | 80.76 | 1.25 | 32 | 8 | 23.48 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/squeezenet/squeezenet_1.0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/squeezenet/squeezenet1_0-24010b28-910v2.ckpt) | -| swin_tiny | 80.90 | 94.90 | 33.38 | 256 | 8 | 466.6 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformer/swin_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swin/swin_tiny-72b3c5e6-910v2.ckpt) | -| swinv2_tiny_window8 | 81.38 | 95.46 | 28.78 | 128 | 8 | 335.18 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swinv2/swinv2_tiny_window8-70c5e903-910v2.ckpt) | -| vgg13 | 72.81 | 91.02 | 133.04 | 32 | 8 | 30.52 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg13-7756f33c-910v2.ckpt) | -| vgg19 | 75.24 | 92.55 | 143.66 | 32 | 8 | 39.17 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg19-5104d1ea-910v2.ckpt) | -| visformer_tiny | 78.40 | 94.30 | 10.33 | 128 | 8 | 201.14 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/visformer/visformer_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/visformer/visformer_tiny-df995ba4-910v2.ckpt) | -| xcit_tiny_12_p16_224 | 77.27 | 93.56 | 7.00 | 128 | 8 | 229.25 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/xcit/xcit_tiny_12_p16_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/xcit/xcit_tiny_12_p16_224-bd90776e-910v2.ckpt) | + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- | +| convit_tiny | 5.71 | 8 | 256 | 224x224 | O2 | 153s | 226.51 | 9022.03 | 73.79 | 91.70 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-1961717e-910v2.ckpt) | +| convnext_tiny | 28.59 | 8 | 16 | 224x224 | O2 | 137s | 48.7 | 2612.24 | 81.28 | 95.61 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnext/convnext_tiny-db11dc82-910v2.ckpt) | +| convnextv2_tiny | 28.64 | 8 | 128 | 224x224 | O2 | 268s | 257.2 | 3984.44 | 82.39 | 95.95 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-a35b79ce-910v2.ckpt) | +| crossvit_9 | 8.55 | 8 | 256 | 240x240 | O2 | 221s | 514.36 | 3984.44 | 73.38 | 91.51 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/crossvit/crossvit_9-32c69c96-910v2.ckpt) | +| densenet121 | 8.06 | 8 | 32 | 224x224 | O2 | 300s | 47,34 | 5446.81 | 75.67 | 92.77 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/densenet/densenet121-bf4ab27f-910v2.ckpt) | +| densenet121 | 8.06 | 8 | 32 | 224x224 | O2 | 300s | 47,34 | 5446.81 | 75.67 | 92.77 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/densenet/densenet121-bf4ab27f-910v2.ckpt) | +| efficientnet_b0 | 5.33 | 8 | 128 | 224x224 | O2 | 353s | 172.64 | 5931.42 | 76.88 | 93.28 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/efficientnet/efficientnet_b0-f8d7aa2a-910v2.ckpt) | +| googlenet | 6.99 | 8 | 32 | 224x224 | O2 | 113s | 23.5 | 10893.62 | 72.89 | 90.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/googlenet/googlenet-de74c31d-910v2.ckpt) | +| googlenet | 6.99 | 8 | 32 | 224x224 | O2 | 113s | 23.5 | 10893.62 | 72.89 | 90.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/googlenet/googlenet-de74c31d-910v2.ckpt) | +| inception_v3 | 27.20 | 8 | 32 | 299x299 | O2 | 172s | 70.83 | 3614.29 | 79.25 | 94.47 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v3/inception_v3-61a8e9ed-910v2.ckpt) | +| inception_v4 | 42.74 | 8 | 32 | 299x299 | O2 | 263s | 80.97 | 3161.66 | 80.98 | 95.25 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v4/inception_v4-56e798fc-910v2.ckpt) | +| mixnet_s | 4.17 | 8 | 128 | 224x224 | O2 | 706s | 228.03 | 4490.64 | 75.58 | 95.54 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mixnet/mixnet_s-fe4fcc63-910v2.ckpt) | +| mnasnet_075 | 3.20 | 8 | 256 | 224x224 | O2 | 144s | 175.85 | 11646.29 | 71.77 | 90.52 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mnasnet/mnasnet_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mnasnet/mnasnet_075-083b2bc4-910v2.ckpt) | +| mobilenet_v1_025 | 0.47 | 8 | 64 | 224x224 | O2 | 195s | 47.47 | 10785.76 | 54.05 | 77.74 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv1/mobilenet_v1_025-cbe3d3b3-910v2.ckpt) | +| mobilenet_v2_075 | 2.66 | 8 | 256 | 224x224 | O2 | 233s | 174.65 | 11726.31 | 69.73 | 89.35 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_075-755932c4-910v2.ckpt) | +| mobilenet_v3_small_100 | 2.55 | 8 | 75 | 224x224 | O2 | 184s | 52.38 | 11454.75 | 68.07 | 87.77 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-6fa3c17d-910v2.ckpt) | +| mobilenet_v3_large_100 | 5.51 | 8 | 75 | 224x224 | O2 | 354s | 55.89 | 10735.37 | 75.59 | 92.57 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-bd4e7bdc-910v2.ckpt) | +| mobilevit_xx_small | 1.27 | 8 | 64 | 256x256 | O2 | 437s | 67.24 | 7614.52 | 67.11 | 87.85 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilevit/mobilevit_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilevit/mobilevit_xx_small-6f2745c3-910v2.ckpt) | +| nasnet_a_4x1056 | 5.33 | 8 | 256 | 224x224 | O2 | 800s | 364.35 | 5620.97 | 74.12 | 91.36 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/nasnet/nasnet_a_4x1056_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/nasnet/nasnet_a_4x1056-015ba575c-910v2.ckpt) | +| pit_ti | 4.85 | 8 | 128 | 224x224 | O2 | 212s | 266.47 | 3842.83 | 73.26 | 91.57 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pit/pit_ti_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pit/pit_ti-33466a0d-910v2.ckpt) | +| poolformer_s12 | 11.92 | 8 | 128 | 224x224 | O2 | 177s | 211.81 | 4834.52 | 77.49 | 93.55 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/poolformer/poolformer_s12_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/poolformer/poolformer_s12-c7e14eea-910v2.ckpt) | +| pvt_tiny | 13.23 | 8 | 128 | 224x224 | O2 | 212s | 237.5 | 4311.58 | 74.88 | 92.12 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvt/pvt_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt/pvt_tiny-6676051f-910v2.ckpt) | +| pvt_v2_b0 | 3.67 | 8 | 128 | 224x224 | O2 | 323s | 255.76 | 4003.75 | 71.25 | 90.50 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvtv2/pvt_v2_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt_v2/pvt_v2_b0-d9cd9d6a-910v2.ckpt) | +| regnet_x_800mf | 7.26 | 8 | 64 | 224x224 | O2 | 228s | 50.74 | 10090.66 | 76.11 | 93.00 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/regnet/regnet_x_800mf_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/regnet/regnet_x_800mf-68fe1cca-910v2.ckpt) | +| repmlp_t224 | 38.30 | 8 | 128 | 224x224 | O2 | 289s | 578.23 | 1770.92 | 76.71 | 93.30 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repmlp/repmlp_t224_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repmlp/repmlp_t224-8dbedd00.ckpt) | +| repvgg_a0 | 9.13 | 8 | 32 | 224x224 | O2 | 76s | 24.12 | 10613.60 | 72.29 | 90.78 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a0-b67a9f15-910v2.ckpt) | +| repvgg_a1 | 14.12 | 8 | 32 | 224x224 | O2 | 81s | 28.29 | 9096.13 | 73.68 | 91.51 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a1-a40aa623-910v2.ckpt) | +| res2net50 | 25.76 | 8 | 32 | 224x224 | O2 | 174s | 39.6 | 6464.65 | 79.33 | 94.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/res2net/res2net_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/res2net/res2net50-aa758355-910v2.ckpt) | +| resnet50 | 25.61 | 8 | 32 | 224x224 | O2 | 77s | 31.9 | 8025.08 | 76.76 | 93.31 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnet/resnet_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnet/resnet50-f369a08d-910v2.ckpt) | +| resnetv2_50 | 25.60 | 8 | 32 | 224x224 | O2 | 120s | 32.19 | 7781.16 | 77.03 | 93.29 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnetv2/resnetv2_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnetv2/resnetv2_50-a0b9f7f8-910v2.ckpt) | +| resnext50_32x4d | 25.10 | 8 | 32 | 224x224 | O2 | 156s | 44.61 | 5738.62 | 78.64 | 94.18 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnext/resnext50_32x4d_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnext/resnext50_32x4d-988f75bc-910v2.ckpt) | +| rexnet_09 | 4.13 | 8 | 64 | 224x224 | O2 | 515s | 115.61 | 3290.28 | 76.14 | 92.96 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/rexnet/rexnet_x09_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/rexnet/rexnet_09-00223eb4-910v2.ckpt) | +| seresnet18 | 11.80 | 8 | 64 | 224x224 | O2 | 90s | 51.09 | 10021.53 | 72.05 | 90.59 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/senet/seresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/senet/seresnet18-7b971c78-910v2.ckpt) | +| shufflenet_v1_g3_05 | 0.73 | 8 | 64 | 224x224 | O2 | 191s | 47.77 | 10718.02 | 57.08 | 79.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv1/shufflenet_v1_g3_05-56209ef3-910v2.ckpt) | +| shufflenet_v2_x0_5 | 1.37 | 8 | 64 | 224x224 | O2 | 100s | 47.32 | 10819.95 | 60.65 | 82.26 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv2/shufflenet_v2_x0_5-39d05bb6-910v2.ckpt) | +| skresnet18 | 11.97 | 8 | 64 | 224x224 | O2 | 134s | 49.83 | 10274.93 | 72.85 | 90.83 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/sknet/skresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/sknet/skresnet18-9d8b1afc-910v2.ckpt) | +| squeezenet1_0 | 1.25 | 8 | 32 | 224x224 | O2 | 64s | 23.48 | 10902.90 | 58.75 | 80.76 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/squeezenet/squeezenet_1.0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/squeezenet/squeezenet1_0-24010b28-910v2.ckpt) | +| swin_tiny | 33.38 | 8 | 256 | 224x224 | O2 | 266s | 466.6 | 4389.20 | 80.90 | 94.90 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformer/swin_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swin/swin_tiny-72b3c5e6-910v2.ckpt) | +| swinv2_tiny_window8 | 28.78 | 8 | 128 | 256x256 | O2 | 385s | 335.18 | 3055.07 | 81.38 | 95.46 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swinv2/swinv2_tiny_window8-70c5e903-910v2.ckpt) | +| vgg13 | 133.04 | 8 | 32 | 224x224 | O2 | 41s | 30.52 | 8387.94 | 72.81 | 91.02 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg13-7756f33c-910v2.ckpt) | +| vgg19 | 143.66 | 8 | 32 | 224x224 | O2 | 53s | 39.17 | 6535.61 | 75.24 | 92.55 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg19-5104d1ea-910v2.ckpt) | +| visformer_tiny | 10.33 | 8 | 128 | 224x224 | O2 | 169s | 201.14 | 5090.98 | 78.40 | 94.30 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/visformer/visformer_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/visformer/visformer_tiny-df995ba4-910v2.ckpt) | +| xcit_tiny_12_p16_224 | 7.00 | 8 | 128 | 224x224 | O2 | 330s | 229.25 | 4466.74 | 77.27 | 93.56 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/xcit/xcit_tiny_12_p16_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/xcit/xcit_tiny_12_p16_224-bd90776e-910v2.ckpt) | diff --git a/configs/README.md b/configs/README.md index c72332b5a..1949ff20c 100644 --- a/configs/README.md +++ b/configs/README.md @@ -33,9 +33,9 @@ Please follow the outline structure and **table format** shown in [densenet/READ
-| model | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe | download | -| ----------- | --------- | --------- | ---------- | ---------- | ----- | ------- | --------- | --------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | -| densenet121 | 75.67 | 92.77 | 8.06 | 32 | 8 | 47,34 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/densenet/densenet121-bf4ab27f-910v2.ckpt) | +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | +| densenet121 | 8.06 | 8 | 32 | 224x224 | O2 | 300s | 47,34 | 5446.81 | 75.67 | 92.77 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/densenet/densenet121-bf4ab27f-910v2.ckpt) |
diff --git a/configs/bit/README.md b/configs/bit/README.md index 075e83596..4364f0656 100644 --- a/configs/bit/README.md +++ b/configs/bit/README.md @@ -2,6 +2,11 @@ > [Big Transfer (BiT): General Visual Representation Learning](https://arxiv.org/abs/1912.11370) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. @@ -13,23 +18,23 @@ BiT use GroupNorm combined with Weight Standardisation instead of BatchNorm. Sin too low. 5) With BiT fine-tuning, good performance can be achieved even if there are only a few examples of each type on natural images.[[1, 2](#References)] -## Results +## Performance Our reproduced model performance on ImageNet-1K is reported as follows. -- ascend 910* with graph mode +- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode *coming soon* -- ascend 910 with graph mode +- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode
-| model | top-1 (%) | top-5 (%) | params(M) | batch size | cards | ms/step | jit_level | recipe | download | -| ------------ | --------- | --------- | --------- | ---------- | ----- |---------| --------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | -| bit_resnet50 | 76.81 | 93.17 | 25.55 | 32 | 8 | 74.52 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/bit/BiT_resnet50-1e4795a4.ckpt) | +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | +| bit_resnet50 | 25.55 | 8 | 32 | 224x224 | O2 | 146s | 74.52 | 3413.33 | 76.81 | 93.17 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/bit/BiT_resnet50-1e4795a4.ckpt) |
diff --git a/configs/cmt/README.md b/configs/cmt/README.md index e531d53d6..c3edf53f6 100644 --- a/configs/cmt/README.md +++ b/configs/cmt/README.md @@ -2,6 +2,11 @@ > [CMT: Convolutional Neural Networks Meet Vision Transformers](https://arxiv.org/abs/2107.06263) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction CMT is a method to make full use of the advantages of CNN and transformers so that the model could capture long-range @@ -10,22 +15,22 @@ and depthwise convolution and pointwise convolution like MobileNet. By combing t on ImageNet-1K dataset. -## Results +## Performance Our reproduced model performance on ImageNet-1K is reported as follows. -- ascend 910* with graph mode +- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode *coming soon* -- ascend 910 with graph mode +- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode
-| model | top-1 (%) | top-5 (%) | params(M) | batch size | cards | ms/step | jit_level | recipe | download | -| --------- | --------- | --------- | --------- | ---------- | ----- |---------| --------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ | -| cmt_small | 83.24 | 96.41 | 26.09 | 128 | 8 | 500.64 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/cmt/cmt_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/cmt/cmt_small-6858ee22.ckpt) | +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ | +| cmt_small | 26.09 | 8 | 128 | 224x224 | O2 | 1268s | 500.64 | 2048.01 | 83.24 | 96.41 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/cmt/cmt_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/cmt/cmt_small-6858ee22.ckpt) |
diff --git a/configs/coat/README.md b/configs/coat/README.md index a78b3d01c..59bedefce 100644 --- a/configs/coat/README.md +++ b/configs/coat/README.md @@ -2,27 +2,32 @@ > [Co-Scale Conv-Attentional Image Transformers](https://arxiv.org/abs/2104.06399v2) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction Co-Scale Conv-Attentional Image Transformer (CoaT) is a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers' encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other. Second, the conv-attentional mechanism is designed by realizing a relative position embedding formulation in the factorized attention module with an efficient convolution-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities. -## Results +## Performance Our reproduced model performance on ImageNet-1K is reported as follows. -- ascend 910* with graph mode +- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode *coming soon* -- ascend 910 with graph mode +- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode
-| model | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe | Weight | -| --------- | --------- | --------- | ---------- | ---------- | ----- |---------| --------- | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | -| coat_tiny | 79.67 | 94.88 | 5.50 | 32 | 8 | 254.95 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_tiny-071cb792.ckpt) | +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | +| coat_tiny | 5.50 | 8 | 32 | 224x224 | O2 | 543s | 254.95 | 1003.92 | 79.67 | 94.88 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_tiny-071cb792.ckpt) |
diff --git a/configs/convit/README.md b/configs/convit/README.md index c322cbb4d..8b6225cc5 100644 --- a/configs/convit/README.md +++ b/configs/convit/README.md @@ -1,6 +1,11 @@ # ConViT > [ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://arxiv.org/abs/2103.10697) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction ConViT combines the strengths of convolutional architectures and Vision Transformers (ViTs). @@ -20,30 +25,30 @@ while offering a much improved sample efficiency.[[1](#references)]

-## Results +## Performance Our reproduced model performance on ImageNet-1K is reported as follows. -- ascend 910* with graph mode +- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode
-| model | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe | download | -| ----------- | --------- | --------- | ---------- | ---------- | ----- | ------- | --------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- | -| convit_tiny | 73.79 | 91.70 | 5.71 | 256 | 8 | 226.51 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-1961717e-910v2.ckpt) | +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- | +| convit_tiny | 5.71 | 8 | 256 | 224x224 | O2 | 153s | 226.51 | 9022.03 | 73.79 | 91.70 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-1961717e-910v2.ckpt) |
-- ascend 910 with graph mode +- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode
-| model | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe | download | -| ----------- | --------- | --------- | ---------- | ---------- | ----- | ------- | --------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- | -| convit_tiny | 73.66 | 91.72 | 5.71 | 256 | 8 | 231.62 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_tiny-e31023f2.ckpt) | +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- | +| convit_tiny | 5.71 | 8 | 256 | 224x224 | O2 | 133s | 231.62 | 8827.59 | 73.66 | 91.72 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_tiny-e31023f2.ckpt) |
diff --git a/configs/convnext/README.md b/configs/convnext/README.md index d5bfcca93..13c6fb4d1 100644 --- a/configs/convnext/README.md +++ b/configs/convnext/README.md @@ -1,6 +1,11 @@ # ConvNeXt > [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction In this work, the authors reexamine the design spaces and test the limits of what a pure ConvNet can achieve. @@ -17,30 +22,30 @@ simplicity and efficiency of standard ConvNets.[[1](#references)] Figure 1. Architecture of ConvNeXt [1]

-## Results +## Performance Our reproduced model performance on ImageNet-1K is reported as follows. -- ascend 910* with graph mode +- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode
-| model | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe | download | -| ------------- | --------- | --------- | ---------- | ---------- | ----- |---------| --------- | ---------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | -| convnext_tiny | 81.28 | 95.61 | 28.59 | 16 | 8 | 48.7 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnext/convnext_tiny-db11dc82-910v2.ckpt) | +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | +| convnext_tiny | 28.59 | 8 | 16 | 224x224 | O2 | 137s | 48.7 | 2612.24 | 81.28 | 95.61 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnext/convnext_tiny-db11dc82-910v2.ckpt) |
-- ascend 910 with graph mode +- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode
-| model | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe | download | -| ------------- | --------- | --------- | ---------- | ---------- | ----- |---------| --------- | ---------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | -| convnext_tiny | 81.91 | 95.79 | 28.59 | 16 | 8 | 66.79 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt) | +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | +| convnext_tiny | 28.59 | 8 | 16 | 224x224 | O2 | 127s | 66.79 | 1910.45 | 81.91 | 95.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt) |
diff --git a/configs/convnextv2/README.md b/configs/convnextv2/README.md index 7deb007a6..267a44551 100644 --- a/configs/convnextv2/README.md +++ b/configs/convnextv2/README.md @@ -1,6 +1,11 @@ # ConvNeXt V2 > [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction In this paper, the authors propose a fully convolutional masked autoencoder framework and a new Global Response @@ -16,28 +21,28 @@ benchmarks, including ImageNet classification, COCO detection, and ADE20K segmen Figure 1. Architecture of ConvNeXt V2 [1]

-## Results +## Performance Our reproduced model performance on ImageNet-1K is reported as follows. -- ascend 910* with graph mode +- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode
-| model | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe | download | -| --------------- | --------- | --------- | ---------- | ---------- | ----- | ------- | --------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | -| convnextv2_tiny | 82.39 | 95.95 | 28.64 | 128 | 8 | 257.2 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-a35b79ce-910v2.ckpt) | +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | +| convnextv2_tiny | 28.64 | 8 | 128 | 224x224 | O2 | 268s | 257.2 | 3984.44 | 82.39 | 95.95 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-a35b79ce-910v2.ckpt) |
-- ascend 910 with graph mode +- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode
-| model | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe | download | -| --------------- | --------- | --------- | ---------- | ---------- | ----- | ------- | --------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | -| convnextv2_tiny | 82.43 | 95.98 | 28.64 | 128 | 8 | 400.20 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) | +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | +| convnextv2_tiny | 28.64 | 8 | 128 | 224x224 | O2 | 237s | 400.20 | 2560.00 | 82.43 | 95.98 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) |
diff --git a/configs/crossvit/README.md b/configs/crossvit/README.md index a1aa17a87..c54348f84 100644 --- a/configs/crossvit/README.md +++ b/configs/crossvit/README.md @@ -1,6 +1,11 @@ # CrossViT > [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://arxiv.org/abs/2103.14899) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction CrossViT is a type of vision transformer that uses a dual-branch architecture to extract multi-scale feature representations for image classification. The architecture combines image patches (i.e. tokens in a transformer) of different sizes to produce stronger visual features for image classification. It processes small and large patch tokens with two separate branches of different computational complexities and these tokens are fused together multiple times to complement each other. @@ -15,29 +20,29 @@ Fusion is achieved by an efficient cross-attention module, in which each transfo

-## Results +## Performance Our reproduced model performance on ImageNet-1K is reported as follows. -- ascend 910* with graph mode +- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode
-| model | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe | download | -| ---------- | --------- | --------- | ---------- | ---------- | ----- | ------- | --------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | -| crossvit_9 | 73.38 | 91.51 | 8.55 | 256 | 8 | 514.36 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/crossvit/crossvit_9-32c69c96-910v2.ckpt) | +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | +| crossvit_9 | 8.55 | 8 | 256 | 240x240 | O2 | 221s | 514.36 | 3984.44 | 73.38 | 91.51 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/crossvit/crossvit_9-32c69c96-910v2.ckpt) |
-- ascend 910 with graph mode +- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode
-| model | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe | download | -| ---------- | --------- | --------- | ---------- | ---------- | ----- | ------- | --------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | -| crossvit_9 | 73.56 | 91.79 | 8.55 | 256 | 8 | 550.79 | O2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/crossvit/crossvit_9-e74c8e18.ckpt) | +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | +| crossvit_9 | 8.55 | 8 | 256 | 240x240 | O2 | 206s | 550.79 | 3719.30 | 73.56 | 91.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/crossvit/crossvit_9-e74c8e18.ckpt) |
diff --git a/configs/densenet/README.md b/configs/densenet/README.md index 668b51115..f0dfd8966 100644 --- a/configs/densenet/README.md +++ b/configs/densenet/README.md @@ -2,6 +2,11 @@ > [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -22,7 +27,7 @@ propagation, encourage feature reuse, and substantially reduce the number of par Figure 1. Architecture of DenseNet [1]

-## Results +## Performance > [Dual Path Networks](https://arxiv.org/abs/1707.01629v2) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -17,7 +22,7 @@ fewer computation cost compared with ResNet and DenseNet on ImageNet-1K dataset. Figure 1. Architecture of DPN [1]

-## Results +## Performance > [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -18,7 +23,7 @@ and resolution scaling could be found. EfficientNet could achieve better model p Figure 1. Architecture of Efficientent [1]

-## Results +## Performance @@ -17,7 +22,7 @@ High-resolution representations are essential for position-sensitive vision prob Figure 1. Architecture of HRNet [1]

-## Results +## Performance > [Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -18,7 +23,7 @@ compared with previous state-of-the-art methods on ImageNet-1K dataset.[[1](#ref Figure 1. Architecture of Nasnet [1]

-## Results +## Performance > [RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -22,7 +27,7 @@ previous methods.[[1](#references)] Figure 1. Architecture of Repvgg [1]

-## Results +## Performance > [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -25,7 +30,7 @@ on ImageNet-1K dataset compared with ViT and ResNet.[[1](#references)] Figure 1. Architecture of Swin Transformer [1]

-## Results +## Performance > [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556) +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -21,7 +26,7 @@ methods such as GoogleLeNet and AlexNet on ImageNet-1K dataset.[[1](#references) Figure 1. Architecture of VGG [1]

-## Results +## Performance @@ -28,7 +33,7 @@ fewer computational resources. [[2](#references)] Figure 1. Architecture of ViT [1]

-## Results +## Performance diff --git a/configs/cmt/README.md b/configs/cmt/README.md index c3edf53f6..41fd3978d 100644 --- a/configs/cmt/README.md +++ b/configs/cmt/README.md @@ -2,10 +2,6 @@ > [CMT: Convolutional Neural Networks Meet Vision Transformers](https://arxiv.org/abs/2107.06263) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Introduction @@ -14,29 +10,11 @@ dependencies and extract local information. In addition, to reduce computation c and depthwise convolution and pointwise convolution like MobileNet. By combing these parts, CMT could get a SOTA performance on ImageNet-1K dataset. +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -*coming soon* - -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ | -| cmt_small | 26.09 | 8 | 128 | 224x224 | O2 | 1268s | 500.64 | 2048.01 | 83.24 | 96.41 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/cmt/cmt_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/cmt/cmt_small-6858ee22.ckpt) | - - -
- -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -83,6 +61,23 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/cmt/cmt_small_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +*coming soon* + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ | +| cmt_small | 26.09 | 8 | 128 | 224x224 | O2 | 1268s | 500.64 | 2048.01 | 83.24 | 96.41 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/cmt/cmt_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/cmt/cmt_small-6858ee22.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/coat/README.md b/configs/coat/README.md index 59bedefce..b60bb82cb 100644 --- a/configs/coat/README.md +++ b/configs/coat/README.md @@ -2,37 +2,15 @@ > [Co-Scale Conv-Attentional Image Transformers](https://arxiv.org/abs/2104.06399v2) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction Co-Scale Conv-Attentional Image Transformer (CoaT) is a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers' encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other. Second, the conv-attentional mechanism is designed by realizing a relative position embedding formulation in the factorized attention module with an efficient convolution-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities. -## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -*coming soon* - - -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | -| coat_tiny | 5.50 | 8 | 32 | 224x224 | O2 | 543s | 254.95 | 1003.92 | 79.67 | 94.88 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_tiny-071cb792.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -79,6 +57,30 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/coat/coat_lite_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +*coming soon* + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | +| coat_tiny | 5.50 | 8 | 32 | 224x224 | O2 | 543s | 254.95 | 1003.92 | 79.67 | 94.88 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_tiny-071cb792.ckpt) | + + + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + + ## References [1] Han D, Yun S, Heo B, et al. Rethinking channel dimensions for efficient model design[C]//Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition. 2021: 732-741. diff --git a/configs/convit/README.md b/configs/convit/README.md index 8b6225cc5..5475c9fcc 100644 --- a/configs/convit/README.md +++ b/configs/convit/README.md @@ -1,10 +1,6 @@ # ConViT > [ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://arxiv.org/abs/2103.10697) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Introduction @@ -24,36 +20,12 @@ while offering a much improved sample efficiency.[[1](#references)] Figure 1. Architecture of ConViT [1]

+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- | -| convit_tiny | 5.71 | 8 | 256 | 224x224 | O2 | 153s | 226.51 | 9022.03 | 73.79 | 91.70 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-1961717e-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- | -| convit_tiny | 5.71 | 8 | 256 | 224x224 | O2 | 133s | 231.62 | 8827.59 | 73.66 | 91.72 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_tiny-e31023f2.ckpt) | - -
-#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -98,6 +70,26 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/convit/convit_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- | +| convit_tiny | 5.71 | 8 | 256 | 224x224 | O2 | 153s | 226.51 | 9022.03 | 73.79 | 91.70 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-1961717e-910v2.ckpt) | + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- | +| convit_tiny | 5.71 | 8 | 256 | 224x224 | O2 | 133s | 231.62 | 8827.59 | 73.66 | 91.72 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_tiny-e31023f2.ckpt) | + + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/convnext/README.md b/configs/convnext/README.md index 13c6fb4d1..db6b075c0 100644 --- a/configs/convnext/README.md +++ b/configs/convnext/README.md @@ -1,11 +1,6 @@ # ConvNeXt > [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction In this work, the authors reexamine the design spaces and test the limits of what a pure ConvNet can achieve. @@ -22,36 +17,12 @@ simplicity and efficiency of standard ConvNets.[[1](#references)] Figure 1. Architecture of ConvNeXt [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | -| convnext_tiny | 28.59 | 8 | 16 | 224x224 | O2 | 137s | 48.7 | 2612.24 | 81.28 | 95.61 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnext/convnext_tiny-db11dc82-910v2.ckpt) | - - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | -| convnext_tiny | 28.59 | 8 | 16 | 224x224 | O2 | 127s | 66.79 | 1910.45 | 81.91 | 95.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt) | - +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -
-#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -97,6 +68,25 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/convnext/convnext_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | +| convnext_tiny | 28.59 | 8 | 16 | 224x224 | O2 | 137s | 48.7 | 2612.24 | 81.28 | 95.61 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnext/convnext_tiny-db11dc82-910v2.ckpt) | + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | +| convnext_tiny | 28.59 | 8 | 16 | 224x224 | O2 | 127s | 66.79 | 1910.45 | 81.91 | 95.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References [1] Liu Z, Mao H, Wu C Y, et al. A convnet for the 2020s[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 11976-11986. diff --git a/configs/convnextv2/README.md b/configs/convnextv2/README.md index 267a44551..b441f6dc0 100644 --- a/configs/convnextv2/README.md +++ b/configs/convnextv2/README.md @@ -1,10 +1,6 @@ # ConvNeXt V2 > [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Introduction @@ -21,33 +17,11 @@ benchmarks, including ImageNet classification, COCO detection, and ADE20K segmen Figure 1. Architecture of ConvNeXt V2 [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | -| convnextv2_tiny | 28.64 | 8 | 128 | 224x224 | O2 | 268s | 257.2 | 3984.44 | 82.39 | 95.95 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-a35b79ce-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | -| convnextv2_tiny | 28.64 | 8 | 128 | 224x224 | O2 | 237s | 400.20 | 2560.00 | 82.43 | 95.98 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -93,6 +67,25 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/convnextv2/convnextv2_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | +| convnextv2_tiny | 28.64 | 8 | 128 | 224x224 | O2 | 268s | 257.2 | 3984.44 | 82.39 | 95.95 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-a35b79ce-910v2.ckpt) | + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | +| convnextv2_tiny | 28.64 | 8 | 128 | 224x224 | O2 | 237s | 400.20 | 2560.00 | 82.43 | 95.98 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References [1] Woo S, Debnath S, Hu R, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders[J]. arXiv preprint arXiv:2301.00808, 2023. diff --git a/configs/crossvit/README.md b/configs/crossvit/README.md index c54348f84..144e9bc68 100644 --- a/configs/crossvit/README.md +++ b/configs/crossvit/README.md @@ -1,11 +1,6 @@ # CrossViT > [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://arxiv.org/abs/2103.14899) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction CrossViT is a type of vision transformer that uses a dual-branch architecture to extract multi-scale feature representations for image classification. The architecture combines image patches (i.e. tokens in a transformer) of different sizes to produce stronger visual features for image classification. It processes small and large patch tokens with two separate branches of different computational complexities and these tokens are fused together multiple times to complement each other. @@ -19,35 +14,11 @@ Fusion is achieved by an efficient cross-attention module, in which each transfo Figure 1. Architecture of CrossViT [1]

+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | -| crossvit_9 | 8.55 | 8 | 256 | 240x240 | O2 | 221s | 514.36 | 3984.44 | 73.38 | 91.51 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/crossvit/crossvit_9-32c69c96-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | -| crossvit_9 | 8.55 | 8 | 256 | 240x240 | O2 | 206s | 550.79 | 3719.30 | 73.56 | 91.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/crossvit/crossvit_9-e74c8e18.ckpt) | - -
- -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -92,6 +63,25 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/crossvit/crossvit_15_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | +| crossvit_9 | 8.55 | 8 | 256 | 240x240 | O2 | 221s | 514.36 | 3984.44 | 73.38 | 91.51 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/crossvit/crossvit_9-32c69c96-910v2.ckpt) | + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | +| crossvit_9 | 8.55 | 8 | 256 | 240x240 | O2 | 206s | 550.79 | 3719.30 | 73.56 | 91.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/crossvit/crossvit_9-e74c8e18.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/densenet/README.md b/configs/densenet/README.md index f0dfd8966..a22fa93f9 100644 --- a/configs/densenet/README.md +++ b/configs/densenet/README.md @@ -2,11 +2,6 @@ > [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction @@ -27,43 +22,10 @@ propagation, encourage feature reuse, and substantially reduce the number of par Figure 1. Architecture of DenseNet [1]

-## Performance - - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - - - -
- -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | -| densenet121 | 8.06 | 8 | 32 | 224x224 | O2 | 300s | 47,34 | 5446.81 | 75.67 | 92.77 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/densenet/densenet121-bf4ab27f-910v2.ckpt) | - -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | -| densenet121 | 8.06 | 8 | 32 | 224x224 | O2 | 191s | 43.28 | 5914.97 | 75.64 | 92.84 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet121-120_5004_Ascend.ckpt) | - - -
- -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Quick Start @@ -109,6 +71,26 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/densenet/densenet_121_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | +| densenet121 | 8.06 | 8 | 32 | 224x224 | O2 | 300s | 47,34 | 5446.81 | 75.67 | 92.77 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/densenet/densenet121-bf4ab27f-910v2.ckpt) | + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | +| densenet121 | 8.06 | 8 | 32 | 224x224 | O2 | 191s | 43.28 | 5914.97 | 75.64 | 92.84 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet121-120_5004_Ascend.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/dpn/README.md b/configs/dpn/README.md index 33307d760..d29c13ebe 100644 --- a/configs/dpn/README.md +++ b/configs/dpn/README.md @@ -2,11 +2,6 @@ > [Dual Path Networks](https://arxiv.org/abs/1707.01629v2) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction @@ -22,36 +17,12 @@ fewer computation cost compared with ResNet and DenseNet on ImageNet-1K dataset. Figure 1. Architecture of DPN [1]

-## Performance - - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -*coming soon* - -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------- | -| dpn92 | 37.79 | 8 | 32 | 224x224 | O2 | 293s | 78.22 | 3272.82 | 79.46 | 94.49 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn92_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn92-e3e0fca.ckpt) | - +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -
-#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -98,6 +69,24 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/dpn/dpn92_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +*coming soon* + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------- | +| dpn92 | 37.79 | 8 | 32 | 224x224 | O2 | 293s | 78.22 | 3272.82 | 79.46 | 94.49 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn92_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn92-e3e0fca.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/edgenext/README.md b/configs/edgenext/README.md index aed70e88f..a52a9dbef 100644 --- a/configs/edgenext/README.md +++ b/configs/edgenext/README.md @@ -2,10 +2,6 @@ > [EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications](https://arxiv.org/abs/2206.10589) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Introduction @@ -22,36 +18,10 @@ to implicitly increase the receptive field and encode multi-scale features.[[1]( Figure 1. Architecture of EdgeNeXt [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | -| edgenext_xx_small | 1.33 | 8 | 256 | 256x256 | O2 | 389s | 239.38 | 8555.43 | 70.64 | 89.75 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/edgenext/edgenext_xx_small-cad13d2c-910v2.ckpt) | - - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | -| edgenext_xx_small | 1.33 | 8 | 256 | 256x256 | O2 | 311s | 191.24 | 10709.06 | 71.02 | 89.99 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_xx_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/edgenext/edgenext_xx_small-afc971fb.ckpt) | - - -
- -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Quick Start @@ -99,6 +69,25 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/edgenext/edgenext_small_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | +| edgenext_xx_small | 1.33 | 8 | 256 | 256x256 | O2 | 389s | 239.38 | 8555.43 | 70.64 | 89.75 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/edgenext/edgenext_xx_small-cad13d2c-910v2.ckpt) | + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | +| edgenext_xx_small | 1.33 | 8 | 256 | 256x256 | O2 | 311s | 191.24 | 10709.06 | 71.02 | 89.99 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_xx_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/edgenext/edgenext_xx_small-afc971fb.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/efficientnet/README.md b/configs/efficientnet/README.md index e28d8981e..b432f6ca6 100644 --- a/configs/efficientnet/README.md +++ b/configs/efficientnet/README.md @@ -2,11 +2,6 @@ > [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction @@ -23,45 +18,11 @@ and resolution scaling could be found. EfficientNet could achieve better model p Figure 1. Architecture of Efficientent [1]

-## Performance - - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | -| efficientnet_b0 | 5.33 | 8 | 128 | 224x224 | O2 | 353s | 172.64 | 5931.42 | 76.88 | 93.28 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/efficientnet/efficientnet_b0-f8d7aa2a-910v2.ckpt) | - - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- | -| efficientnet_b0 | 5.33 | 8 | 128 | 224x224 | O2 | 203s | 172.78 | 5926.61 | 76.89 | 93.16 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/efficientnet/efficientnet_b0-103ec70c.ckpt) | - - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -108,6 +69,26 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/efficientnet/efficientnet_b0_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | +| efficientnet_b0 | 5.33 | 8 | 128 | 224x224 | O2 | 353s | 172.64 | 5931.42 | 76.88 | 93.28 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/efficientnet/efficientnet_b0-f8d7aa2a-910v2.ckpt) | + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- | +| efficientnet_b0 | 5.33 | 8 | 128 | 224x224 | O2 | 203s | 172.78 | 5926.61 | 76.89 | 93.16 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/efficientnet/efficientnet_b0-103ec70c.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + + ## References diff --git a/configs/ghostnet/README.md b/configs/ghostnet/README.md index c6e94ac77..cd9777ba7 100644 --- a/configs/ghostnet/README.md +++ b/configs/ghostnet/README.md @@ -1,11 +1,6 @@ # GhostNet > [GhostNet: More Features from Cheap Operations](https://arxiv.org/abs/1911.11907) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been @@ -26,28 +21,10 @@ dataset.[[1](#references)] Figure 1. Architecture of GhostNet [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -*coming soon* - -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | -| ghostnet_050 | 2.60 | 8 | 128 | 224x224 | O2 | 383s | 211.13 | 4850.09 | 66.03 | 86.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_050_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_050-85b91860.ckpt) | - - -
- -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Quick Start @@ -94,6 +71,23 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/ghostnet/ghostnet_100_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +*coming soon* + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | +| ghostnet_050 | 2.60 | 8 | 128 | 224x224 | O2 | 383s | 211.13 | 4850.09 | 66.03 | 86.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_050_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_050-85b91860.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References [1] Han K, Wang Y, Tian Q, et al. Ghostnet: More features from cheap operations[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 1580-1589. diff --git a/configs/googlenet/README.md b/configs/googlenet/README.md index 1edd59dd2..13ba0310c 100644 --- a/configs/googlenet/README.md +++ b/configs/googlenet/README.md @@ -1,11 +1,6 @@ # GoogLeNet > [GoogLeNet: Going Deeper with Convolutions](https://arxiv.org/abs/1409.4842) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction GoogLeNet is a new deep learning structure proposed by Christian Szegedy in 2014. Prior to this, AlexNet, VGG and other @@ -22,35 +17,10 @@ training results.[[1](#references)] Figure 1. Architecture of GoogLeNet [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | -| googlenet | 6.99 | 8 | 32 | 224x224 | O2 | 113s | 23.5 | 10893.62 | 72.89 | 90.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/googlenet/googlenet-de74c31d-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | -| googlenet | 6.99 | 8 | 32 | 224x224 | O2 | 72s | 21.40 | 11962.62 | 72.68 | 90.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/googlenet/googlenet-5552fcd3.ckpt) | - -
- -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Quick Start @@ -97,6 +67,25 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/googlenet/googlenet_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | +| googlenet | 6.99 | 8 | 32 | 224x224 | O2 | 113s | 23.5 | 10893.62 | 72.89 | 90.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/googlenet/googlenet-de74c31d-910v2.ckpt) | + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | +| googlenet | 6.99 | 8 | 32 | 224x224 | O2 | 72s | 21.40 | 11962.62 | 72.68 | 90.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/googlenet/googlenet-5552fcd3.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References [1] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1-9. diff --git a/configs/halonet/README.md b/configs/halonet/README.md index 4b8785487..3f9340122 100644 --- a/configs/halonet/README.md +++ b/configs/halonet/README.md @@ -2,10 +2,6 @@ > [Scaling Local Self-Attention for Parameter Efficient Visual Backbones](https://arxiv.org/abs/2103.12731) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Introduction @@ -29,28 +25,11 @@ Down Sampling：In order to reduce the amount of computation, each block is samp Figure 2. Architecture of Down Sampling [1]

+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -*coming soon* - -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | -| halonet_50t | 22.79 | 8 | 64 | 256x256 | O2 | 261s | 421.66 | 6437.82 | 79.53 | 94.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/halonet/halonet_50t_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/halonet/halonet_50t-533da6be.ckpt) | - -
- -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -97,6 +76,21 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/halonet/halonet_50t_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +*coming soon* + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +*coming soon* + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References [1] Vaswani A, Ramachandran P, Srinivas A, et al. Scaling local self-attention for parameter efficient visual backbones[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 12894-12904. diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md index 15a8abe21..6e2540fa8 100644 --- a/configs/hrnet/README.md +++ b/configs/hrnet/README.md @@ -3,10 +3,6 @@ > [Deep High-Resolution Representation Learning for Visual Recognition](https://arxiv.org/abs/1908.07919) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Introduction @@ -22,47 +18,10 @@ High-resolution representations are essential for position-sensitive vision prob Figure 1. Architecture of HRNet [1]

-## Performance - - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | -| hrnet_w32 | 41.30 | 8 | 128 | 224x224 | O2 | 1069s | 238.03 | 4301.98 | 80.66 | 95.30 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/hrnet/hrnet_w32_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/hrnet/hrnet_w32-e616cdcb-910v2.ckpt) | - - - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | -| hrnet_w32 | 41.30 | 128 | 8 | 224x224 | O2 | 1312s | 279.10 | 3668.94 | 80.64 | 95.44 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/hrnet/hrnet_w32_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/hrnet/hrnet_w32-cc4fbd91.ckpt) | - - - -
- -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. - +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Quick Start ### Preparation @@ -108,6 +67,25 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/hrnet/hrnet_w32_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | +| hrnet_w32 | 41.30 | 8 | 128 | 224x224 | O2 | 1069s | 238.03 | 4301.98 | 80.66 | 95.30 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/hrnet/hrnet_w32_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/hrnet/hrnet_w32-e616cdcb-910v2.ckpt) | + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | +| hrnet_w32 | 41.30 | 128 | 8 | 224x224 | O2 | 1312s | 279.10 | 3668.94 | 80.64 | 95.44 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/hrnet/hrnet_w32_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/hrnet/hrnet_w32-cc4fbd91.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/inceptionv3/README.md b/configs/inceptionv3/README.md index a5f55eb8d..b41814de7 100644 --- a/configs/inceptionv3/README.md +++ b/configs/inceptionv3/README.md @@ -1,11 +1,6 @@ # InceptionV3 > [InceptionV3: Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/pdf/1512.00567.pdf) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction InceptionV3 is an upgraded version of GoogLeNet. One of the most important improvements of V3 is Factorization, which @@ -23,35 +18,12 @@ regularization and effectively reduces overfitting.[[1](#references)] Figure 1. Architecture of InceptionV3 [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | -| inception_v3 | 27.20 | 8 | 32 | 299x299 | O2 | 172s | 70.83 | 3614.29 | 79.25 | 94.47 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v3/inception_v3-61a8e9ed-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------ | -| inception_v3 | 27.20 | 8 | 32 | 299x299 | O2 | 120s | 76.42 | 3349.91 | 79.11 | 94.40 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v3/inception_v3-38f67890.ckpt) | +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -
-#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -98,6 +70,25 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/inceptionv3/inception_v3_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | +| inception_v3 | 27.20 | 8 | 32 | 299x299 | O2 | 172s | 70.83 | 3614.29 | 79.25 | 94.47 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v3/inception_v3-61a8e9ed-910v2.ckpt) | + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------ | +| inception_v3 | 27.20 | 8 | 32 | 299x299 | O2 | 120s | 76.42 | 3349.91 | 79.11 | 94.40 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v3/inception_v3-38f67890.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References [1] Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2818-2826. diff --git a/configs/inceptionv4/README.md b/configs/inceptionv4/README.md index a68b0396e..6eaa4718f 100644 --- a/configs/inceptionv4/README.md +++ b/configs/inceptionv4/README.md @@ -1,11 +1,6 @@ # InceptionV4 > [InceptionV4: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/pdf/1602.07261.pdf) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction InceptionV4 studies whether the Inception module combined with Residual Connection can be improved. It is found that the @@ -20,34 +15,11 @@ performance with Inception-ResNet v2.[[1](#references)] Figure 1. Architecture of InceptionV4 [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------ | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | -| inception_v4 | 42.74 | 8 | 32 | 299x299 | O2 | 263s | 80.97 | 3161.66 | 80.98 | 95.25 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v4/inception_v4-56e798fc-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------ | -| inception_v4 | 42.74 | 8 | 32 | 299x299 | O2 | 177s | 76.19 | 3360.02 | 80.88 | 95.34 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v4/inception_v4-db9c45b3.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -94,6 +66,25 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/inceptionv4/inception_v4_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------ | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | +| inception_v4 | 42.74 | 8 | 32 | 299x299 | O2 | 263s | 80.97 | 3161.66 | 80.98 | 95.25 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v4/inception_v4-56e798fc-910v2.ckpt) | + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------ | +| inception_v4 | 42.74 | 8 | 32 | 299x299 | O2 | 177s | 76.19 | 3360.02 | 80.88 | 95.34 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v4/inception_v4-db9c45b3.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/mixnet/README.md b/configs/mixnet/README.md index 81070b628..6cb91e413 100644 --- a/configs/mixnet/README.md +++ b/configs/mixnet/README.md @@ -1,10 +1,7 @@ # MixNet > [MixConv: Mixed Depthwise Convolutional Kernels](https://arxiv.org/abs/1907.09595) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -22,36 +19,10 @@ and efficiency for existing MobileNets on both ImageNet classification and COCO Figure 1. Architecture of MixNet [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | -| mixnet_s | 4.17 | 8 | 128 | 224x224 | O2 | 706s | 228.03 | 4490.64 | 75.58 | 95.54 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mixnet/mixnet_s-fe4fcc63-910v2.ckpt) | - - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | -| mixnet_s | 4.17 | 8 | 128 | 224x224 | O2 | 556s | 252.49 | 4055.61 | 75.52 | 92.52 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mixnet/mixnet_s-2a5ef3a3.ckpt) | - - -
- -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Quick Start @@ -98,6 +69,27 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/mixnet/mixnet_s_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | +| mixnet_s | 4.17 | 8 | 128 | 224x224 | O2 | 706s | 228.03 | 4490.64 | 75.58 | 95.54 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mixnet/mixnet_s-fe4fcc63-910v2.ckpt) | + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | +| mixnet_s | 4.17 | 8 | 128 | 224x224 | O2 | 556s | 252.49 | 4055.61 | 75.52 | 92.52 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mixnet/mixnet_s-2a5ef3a3.ckpt) | + + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/mnasnet/README.md b/configs/mnasnet/README.md index c1515f6ba..fee586e0a 100644 --- a/configs/mnasnet/README.md +++ b/configs/mnasnet/README.md @@ -1,10 +1,7 @@ # MnasNet > [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://arxiv.org/abs/1807.11626) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -17,36 +14,12 @@ Designing convolutional neural networks (CNN) for mobile devices is challenging Figure 1. Architecture of MnasNet [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | -| mnasnet_075 | 3.20 | 8 | 256 | 224x224 | O2 | 144s | 175.85 | 11646.29 | 71.77 | 90.52 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mnasnet/mnasnet_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mnasnet/mnasnet_075-083b2bc4-910v2.ckpt) | - - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | -| mnasnet_075 | 3.20 | 8 | 256 | 224x224 | O2 | 140s | 165.43 | 12379.86 | 71.81 | 90.53 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mnasnet/mnasnet_0.75_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mnasnet/mnasnet_075-465d366d.ckpt) | - +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -
-#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -93,6 +66,27 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/mnasnet/mnasnet_0.75_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | +| mnasnet_075 | 3.20 | 8 | 256 | 224x224 | O2 | 144s | 175.85 | 11646.29 | 71.77 | 90.52 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mnasnet/mnasnet_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mnasnet/mnasnet_075-083b2bc4-910v2.ckpt) | + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | +| mnasnet_075 | 3.20 | 8 | 256 | 224x224 | O2 | 140s | 165.43 | 12379.86 | 71.81 | 90.53 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mnasnet/mnasnet_0.75_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mnasnet/mnasnet_075-465d366d.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/mobilenetv1/README.md b/configs/mobilenetv1/README.md index ea370c249..51c9d914e 100644 --- a/configs/mobilenetv1/README.md +++ b/configs/mobilenetv1/README.md @@ -1,11 +1,6 @@ # MobileNetV1 > [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction Compared with the traditional convolutional neural network, MobileNetV1's parameters and the amount of computation are greatly reduced on the premise that the accuracy rate is slightly reduced. (Compared to VGG16, the accuracy rate is reduced by 0.9%, but the model parameters are only 1/32 of VGG). The model is based on a streamlined architecture that uses depthwise separable convolutions to build lightweight deep neural networks. At the same time, two simple global hyperparameters are introduced, which can effectively trade off latency and accuracy.[[1](#references)] @@ -17,36 +12,11 @@ Compared with the traditional convolutional neural network, MobileNetV1's parame Figure 1. Architecture of MobileNetV1 [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | -| mobilenet_v1_025 | 0.47 | 8 | 64 | 224x224 | O2 | 195s | 47.47 | 10785.76 | 54.05 | 77.74 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv1/mobilenet_v1_025-cbe3d3b3-910v2.ckpt) | - - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | -| mobilenet_v1_025 | 0.47 | 8 | 64 | 224x224 | O2 | 89s | 42.43 | 12066.93 | 53.87 | 77.66 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv1/mobilenet_v1_025-d3377fba.ckpt) | - - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -93,6 +63,24 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | +| mobilenet_v1_025 | 0.47 | 8 | 64 | 224x224 | O2 | 195s | 47.47 | 10785.76 | 54.05 | 77.74 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv1/mobilenet_v1_025-cbe3d3b3-910v2.ckpt) | + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | +| mobilenet_v1_025 | 0.47 | 8 | 64 | 224x224 | O2 | 89s | 42.43 | 12066.93 | 53.87 | 77.66 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv1/mobilenet_v1_025-d3377fba.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/mobilenetv2/README.md b/configs/mobilenetv2/README.md index 1e588bcbf..932de95c6 100644 --- a/configs/mobilenetv2/README.md +++ b/configs/mobilenetv2/README.md @@ -1,11 +1,6 @@ # MobileNetV2 > [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction The model is a new neural network architecture that is specifically tailored for mobile and resource-constrained environments. This network pushes the state of the art for mobile custom computer vision models, significantly reducing the amount of operations and memory required while maintaining the same accuracy. @@ -19,36 +14,12 @@ The main innovation of the model is the proposal of a new layer module: The Inve Figure 1. Architecture of MobileNetV2 [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | -| mobilenet_v2_075 | 2.66 | 8 | 256 | 224x224 | O2 | 233s | 174.65 | 11726.31 | 69.73 | 89.35 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_075-755932c4-910v2.ckpt) | - - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | -| mobilenet_v2_075 | 2.66 | 8 | 256 | 224x224 | O2 | 164s | 155.94 | 13133.26 | 69.98 | 89.32 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_075-bd7bd4c4.ckpt) | - +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -
-#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -95,6 +66,26 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | +| mobilenet_v2_075 | 2.66 | 8 | 256 | 224x224 | O2 | 233s | 174.65 | 11726.31 | 69.73 | 89.35 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_075-755932c4-910v2.ckpt) | + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | +| mobilenet_v2_075 | 2.66 | 8 | 256 | 224x224 | O2 | 164s | 155.94 | 13133.26 | 69.98 | 89.32 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_075-bd7bd4c4.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/mobilenetv3/README.md b/configs/mobilenetv3/README.md index 376a6f321..62c119af8 100644 --- a/configs/mobilenetv3/README.md +++ b/configs/mobilenetv3/README.md @@ -1,10 +1,7 @@ # MobileNetV3 > [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -19,36 +16,12 @@ mobilenet-v3 offers two versions, mobilenet-v3 large and mobilenet-v3 small, for Figure 1. Architecture of MobileNetV3 [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------- | -| mobilenet_v3_small_100 | 2.55 | 8 | 75 | 224x224 | O2 | 184s | 52.38 | 11454.75 | 68.07 | 87.77 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-6fa3c17d-910v2.ckpt) | -| mobilenet_v3_large_100 | 5.51 | 8 | 75 | 224x224 | O2 | 354s | 55.89 | 10735.37 | 75.59 | 92.57 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-bd4e7bdc-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------- | -| mobilenet_v3_small_100 | 2.55 | 8 | 75 | 224x224 | O2 | 145s | 48.14 | 12463.65 | 68.10 | 87.86 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-509c6047.ckpt) | -| mobilenet_v3_large_100 | 5.51 | 8 | 75 | 224x224 | O2 | 271s | 47.49 | 12634.24 | 75.23 | 92.31 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-1279ad5f.ckpt) | +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -
-#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -95,6 +68,28 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/mobilenetv3/mobilenet_v3_small_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------- | +| mobilenet_v3_small_100 | 2.55 | 8 | 75 | 224x224 | O2 | 184s | 52.38 | 11454.75 | 68.07 | 87.77 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-6fa3c17d-910v2.ckpt) | +| mobilenet_v3_large_100 | 5.51 | 8 | 75 | 224x224 | O2 | 354s | 55.89 | 10735.37 | 75.59 | 92.57 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-bd4e7bdc-910v2.ckpt) | + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------- | +| mobilenet_v3_small_100 | 2.55 | 8 | 75 | 224x224 | O2 | 145s | 48.14 | 12463.65 | 68.10 | 87.86 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-509c6047.ckpt) | +| mobilenet_v3_large_100 | 5.51 | 8 | 75 | 224x224 | O2 | 271s | 47.49 | 12634.24 | 75.23 | 92.31 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-1279ad5f.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/mobilevit/README.md b/configs/mobilevit/README.md index 3a68dc6c5..53104cffd 100644 --- a/configs/mobilevit/README.md +++ b/configs/mobilevit/README.md @@ -1,10 +1,6 @@ # MobileViT > [MobileViT：Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/pdf/2110.02178.pdf) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Introduction @@ -17,36 +13,12 @@ MobileViT, a light-weight and general-purpose vision transformer for mobile devi Figure 1. Architecture of MobileViT [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | -| mobilevit_xx_small | 1.27 | 8 | 64 | 256x256 | O2 | 437s | 67.24 | 7614.52 | 67.11 | 87.85 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilevit/mobilevit_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilevit/mobilevit_xx_small-6f2745c3-910v2.ckpt) | - - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- | -| mobilevit_xx_small | 1.27 | 64 | 8 | 256x256 | O2 | 301s | 53.52 | 9566.52 | 68.91 | 88.91 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilevit/mobilevit_xx_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilevit/mobilevit_xx_small-af9da8a0.ckpt) | - +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -
-#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -91,3 +63,25 @@ To validate the accuracy of the trained model, you can use `validate.py` and par ``` python validate.py -c configs/mobilevit/mobilevit_xx_small_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` + +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | +| mobilevit_xx_small | 1.27 | 8 | 64 | 256x256 | O2 | 437s | 67.24 | 7614.52 | 67.11 | 87.85 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilevit/mobilevit_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilevit/mobilevit_xx_small-6f2745c3-910v2.ckpt) | + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- | +| mobilevit_xx_small | 1.27 | 64 | 8 | 256x256 | O2 | 301s | 53.52 | 9566.52 | 68.91 | 88.91 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilevit/mobilevit_xx_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilevit/mobilevit_xx_small-af9da8a0.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/configs/nasnet/README.md b/configs/nasnet/README.md index 26bde0687..6a63eb22b 100644 --- a/configs/nasnet/README.md +++ b/configs/nasnet/README.md @@ -2,10 +2,6 @@ > [Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Introduction @@ -23,42 +19,12 @@ compared with previous state-of-the-art methods on ImageNet-1K dataset.[[1](#ref Figure 1. Architecture of Nasnet [1]

-## Performance - - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | -| nasnet_a_4x1056 | 5.33 | 8 | 256 | 224x224 | O2 | 800s | 364.35 | 5620.97 | 74.12 | 91.36 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/nasnet/nasnet_a_4x1056_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/nasnet/nasnet_a_4x1056-015ba575c-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | -| nasnet_a_4x1056 | 5.33 | 8 | 256 | 224x224 | O2 | 656s | 330.89 | 6189.37 | 73.65 | 91.25 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/nasnet/nasnet_a_4x1056_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/nasnet/nasnet_a_4x1056-0fbb5cdd.ckpt) | +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -
-#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -105,6 +71,28 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/nasnet/nasnet_a_4x1056_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | +| nasnet_a_4x1056 | 5.33 | 8 | 256 | 224x224 | O2 | 800s | 364.35 | 5620.97 | 74.12 | 91.36 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/nasnet/nasnet_a_4x1056_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/nasnet/nasnet_a_4x1056-015ba575c-910v2.ckpt) | + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | +| nasnet_a_4x1056 | 5.33 | 8 | 256 | 224x224 | O2 | 656s | 330.89 | 6189.37 | 73.65 | 91.25 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/nasnet/nasnet_a_4x1056_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/nasnet/nasnet_a_4x1056-0fbb5cdd.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/pit/README.md b/configs/pit/README.md index 9a54c585a..5c4ff67a2 100644 --- a/configs/pit/README.md +++ b/configs/pit/README.md @@ -1,11 +1,6 @@ # PiT > [PiT: Rethinking Spatial Dimensions of Vision Transformers](https://arxiv.org/abs/2103.16302v2) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction PiT (Pooling-based Vision Transformer) is an improvement of Vision Transformer (ViT) model proposed by Byeongho Heo in 2021. PiT adds pooling layer on the basis of ViT model, so that the spatial dimension of each layer is reduced like CNN, instead of ViT using the same spatial dimension for all layers. PiT achieves the improved model capability and generalization performance against ViT. [[1](#references)] @@ -19,36 +14,12 @@ PiT (Pooling-based Vision Transformer) is an improvement of Vision Transformer ( Figure 1. Architecture of PiT [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | -| pit_ti | 4.85 | 8 | 128 | 224x224 | O2 | 212s | 266.47 | 3842.83 | 73.26 | 91.57 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pit/pit_ti_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pit/pit_ti-33466a0d-910v2.ckpt) | - - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | -| pit_ti | 4.85 | 8 | 128 | 224x224 | O2 | 192s | 271.50 | 3771.64 | 72.96 | 91.33 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pit/pit_ti_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pit/pit_ti-e647a593.ckpt) | - +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -
-#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -95,6 +66,37 @@ To validate the accuracy of the trained model, you can use `validate.py` and par python validate.py -c configs/pit/pit_xs_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | +| pit_ti | 4.85 | 8 | 128 | 224x224 | O2 | 212s | 266.47 | 3842.83 | 73.26 | 91.57 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pit/pit_ti_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pit/pit_ti-33466a0d-910v2.ckpt) | + + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | +| pit_ti | 4.85 | 8 | 128 | 224x224 | O2 | 192s | 271.50 | 3771.64 | 72.96 | 91.33 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pit/pit_ti_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pit/pit_ti-e647a593.ckpt) | + + + + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/poolformer/README.md b/configs/poolformer/README.md index 2d2647dc0..c37e5a63d 100644 --- a/configs/poolformer/README.md +++ b/configs/poolformer/README.md @@ -2,10 +2,7 @@ > [MetaFormer Is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -17,35 +14,12 @@ Figure 1. MetaFormer and performance of MetaFormer-based models on ImageNet-1K v ![PoolFormer](https://user-images.githubusercontent.com/74176172/210046845-6caa1574-b6a4-47f3-8298-c8ca3b4f8fa4.png) Figure 2. (a) The overall framework of PoolFormer. (b) The architecture of PoolFormer block. Compared with Transformer block, it replaces attention with an extremely simple non-parametric operator, pooling, to conduct only basic token mixing.[[1](#References)] -## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| -------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | -| poolformer_s12 | 11.92 | 8 | 128 | 224x224 | O2 | 177s | 211.81 | 4834.52 | 77.49 | 93.55 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/poolformer/poolformer_s12_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/poolformer/poolformer_s12-c7e14eea-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------ | -| poolformer_s12 | 11.92 | 8 | 128 | 224x224 | O2 | 118s | 220.13 | 4651.80 | 77.33 | 93.34 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/poolformer/poolformer_s12_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/poolformer/poolformer_s12-5be5c4e4.ckpt) | +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -
-#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -91,6 +65,27 @@ python train.py --config configs/poolformer/poolformer_s12_ascend.yaml --data_di validation of poolformer has to be done in amp O3 mode which is not supported, coming soon... ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| -------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | +| poolformer_s12 | 11.92 | 8 | 128 | 224x224 | O2 | 177s | 211.81 | 4834.52 | 77.49 | 93.55 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/poolformer/poolformer_s12_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/poolformer/poolformer_s12-c7e14eea-910v2.ckpt) | + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------ | +| poolformer_s12 | 11.92 | 8 | 128 | 224x224 | O2 | 118s | 220.13 | 4651.80 | 77.33 | 93.34 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/poolformer/poolformer_s12_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/poolformer/poolformer_s12-5be5c4e4.ckpt) | + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References [1]. Yu W, Luo M, Zhou P, et al. Metaformer is actually what you need for vision[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 10819-10829. diff --git a/configs/pvt/README.md b/configs/pvt/README.md index 9d9c6d7fb..f724d9e7b 100644 --- a/configs/pvt/README.md +++ b/configs/pvt/README.md @@ -2,10 +2,7 @@ > [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/abs/2102.12122) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -17,35 +14,11 @@ overhead.[[1](#References)] ![PVT](https://user-images.githubusercontent.com/74176172/210046926-2322161b-a963-4603-b3cb-86ecdca41262.png) -## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------- | -| pvt_tiny | 13.23 | 8 | 128 | 224x224 | O2 | 212s | 237.5 | 4311.58 | 74.88 | 92.12 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvt/pvt_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt/pvt_tiny-6676051f-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------- | -| pvt_tiny | 13.23 | 8 | 128 | 224x224 | O2 | 192s | 229.63 | 4459.35 | 74.81 | 92.18 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvt/pvt_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pvt/pvt_tiny-6abb953d.ckpt) | - -
- -#### Notes +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -101,6 +74,28 @@ with `--ckpt_path`. python validate.py --model=pvt_tiny --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------- | +| pvt_tiny | 13.23 | 8 | 128 | 224x224 | O2 | 212s | 237.5 | 4311.58 | 74.88 | 92.12 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvt/pvt_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt/pvt_tiny-6676051f-910v2.ckpt) | + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------- | +| pvt_tiny | 13.23 | 8 | 128 | 224x224 | O2 | 192s | 229.63 | 4459.35 | 74.81 | 92.18 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvt/pvt_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pvt/pvt_tiny-6abb953d.ckpt) | + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/pvtv2/README.md b/configs/pvtv2/README.md index 78e258d8a..872dac752 100644 --- a/configs/pvtv2/README.md +++ b/configs/pvtv2/README.md @@ -2,11 +2,6 @@ > [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://arxiv.org/abs/2106.13797) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction In this work, the authors present new baselines by improving the original Pyramid Vision Transformer (PVT v1) by adding @@ -22,35 +17,10 @@ segmentation.[[1](#references)] Figure 1. Architecture of PVTV2 [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | -| pvt_v2_b0 | 3.67 | 8 | 128 | 224x224 | O2 | 323s | 255.76 | 4003.75 | 71.25 | 90.50 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvtv2/pvt_v2_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt_v2/pvt_v2_b0-d9cd9d6a-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | -| pvt_v2_b0 | 3.67 | 8 | 128 | 224x224 | O2 | 269s | 269.38 | 3801.32 | 71.50 | 90.60 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvtv2/pvt_v2_b0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pvt_v2/pvt_v2_b0-1c4f6683.ckpt) | - -
- -#### Notes - -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Quick Start @@ -104,6 +74,29 @@ with `--ckpt_path`. python validate.py -c configs/pvtv2/pvt_v2_b0_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | +| pvt_v2_b0 | 3.67 | 8 | 128 | 224x224 | O2 | 323s | 255.76 | 4003.75 | 71.25 | 90.50 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvtv2/pvt_v2_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt_v2/pvt_v2_b0-d9cd9d6a-910v2.ckpt) | + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | +| pvt_v2_b0 | 3.67 | 8 | 128 | 224x224 | O2 | 269s | 269.38 | 3801.32 | 71.50 | 90.60 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvtv2/pvt_v2_b0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pvt_v2/pvt_v2_b0-1c4f6683.ckpt) | + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/regnet/README.md b/configs/regnet/README.md index faffe3e8d..6bcc738b0 100644 --- a/configs/regnet/README.md +++ b/configs/regnet/README.md @@ -2,11 +2,6 @@ > [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction In this work, the authors present a new network design paradigm that combines the advantages of manual design and NAS. @@ -26,35 +21,10 @@ has a higher concentration of good models.[[1](#References)] ![RegNet](https://user-images.githubusercontent.com/74176172/210046899-4e83bb56-f7f6-49b2-9dde-dce200428e92.png) -## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | -| regnet_x_800mf | 7.26 | 8 | 64 | 224x224 | O2 | 228s | 50.74 | 10090.66 | 76.11 | 93.00 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/regnet/regnet_x_800mf_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/regnet/regnet_x_800mf-68fe1cca-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | -| regnet_x_800mf | 7.26 | 8 | 64 | 224x224 | O2 | 99s | 42.49 | 12049.89 | 76.04 | 92.97 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/regnet/regnet_x_800mf_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/regnet/regnet_x_800mf-617227f4.ckpt) | - -
- -#### Notes - -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Quick Start @@ -106,7 +76,28 @@ with `--ckpt_path`. ```shell python validate.py --model=regnet_x_800mf --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | +| regnet_x_800mf | 7.26 | 8 | 64 | 224x224 | O2 | 228s | 50.74 | 10090.66 | 76.11 | 93.00 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/regnet/regnet_x_800mf_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/regnet/regnet_x_800mf-68fe1cca-910v2.ckpt) | + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | +| regnet_x_800mf | 7.26 | 8 | 64 | 224x224 | O2 | 99s | 42.49 | 12049.89 | 76.04 | 92.97 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/regnet/regnet_x_800mf_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/regnet/regnet_x_800mf-617227f4.ckpt) | + + +### Notes +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/repmlp/README.md b/configs/repmlp/README.md index 69ede006f..048b1d904 100644 --- a/configs/repmlp/README.md +++ b/configs/repmlp/README.md @@ -2,11 +2,6 @@ > [RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality](https://arxiv.org/abs/2112.11081) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | - ## Introduction Compared to convolutional layers, fully-connected (FC) layers are better at modeling the long-range dependencies @@ -29,28 +24,11 @@ segmentation. ![RepMLP](https://user-images.githubusercontent.com/74176172/210046952-c4f05321-76e9-4d7a-b419-df91aac64cdf.png) Figure 1. RepMLP Block.[[1](#References)] -## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -*coming soon* - -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- | -| repmlp_t224 | 38.30 | 8 | 128 | 224x224 | O2 | 289s | 578.23 | 1770.92 | 76.71 | 93.30 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repmlp/repmlp_t224_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repmlp/repmlp_t224-8dbedd00.ckpt) | - -
- -#### Notes +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -103,6 +81,24 @@ with `--ckpt_path`. python validate.py --model=repmlp_t224 --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +*coming soon* + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- | +| repmlp_t224 | 38.30 | 8 | 128 | 224x224 | O2 | 289s | 578.23 | 1770.92 | 76.71 | 93.30 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repmlp/repmlp_t224_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repmlp/repmlp_t224-8dbedd00.ckpt) | + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/repvgg/README.md b/configs/repvgg/README.md index a108040c1..b1b5ac7bf 100644 --- a/configs/repvgg/README.md +++ b/configs/repvgg/README.md @@ -3,10 +3,7 @@ > [RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -27,46 +24,12 @@ previous methods.[[1](#references)] Figure 1. Architecture of Repvgg [1]

-## Performance - - - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | -| repvgg_a0 | 9.13 | 8 | 32 | 224x224 | O2 | 76s | 24.12 | 10613.60 | 72.29 | 90.78 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a0-b67a9f15-910v2.ckpt) | -| repvgg_a1 | 14.12 | 8 | 32 | 224x224 | O2 | 81s | 28.29 | 9096.13 | 73.68 | 91.51 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a1-a40aa623-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | -| repvgg_a0 | 9.13 | 8 | 32 | 224x224 | O2 | 50s
| 20.58 | 12439.26 | 72.19 | 90.75 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a0-6e71139d.ckpt) | -| repvgg_a1 | 14.12 | 8 | 32 | 224x224 | O2 | 29s | 20.70 | 12367.15 | 74.19 | 91.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a1-539513ac.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -122,6 +85,31 @@ python validate.py -c configs/repvgg/repvgg_a1_ascend.yaml --data_dir /path/to/i ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | +| repvgg_a0 | 9.13 | 8 | 32 | 224x224 | O2 | 76s | 24.12 | 10613.60 | 72.29 | 90.78 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a0-b67a9f15-910v2.ckpt) | +| repvgg_a1 | 14.12 | 8 | 32 | 224x224 | O2 | 81s | 28.29 | 9096.13 | 73.68 | 91.51 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a1-a40aa623-910v2.ckpt) | + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | +| repvgg_a0 | 9.13 | 8 | 32 | 224x224 | O2 | 50s
| 20.58 | 12439.26 | 72.19 | 90.75 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a0-6e71139d.ckpt) | +| repvgg_a1 | 14.12 | 8 | 32 | 224x224 | O2 | 29s | 20.70 | 12367.15 | 74.19 | 91.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a1-539513ac.ckpt) | + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/res2net/README.md b/configs/res2net/README.md index b074c83be..db15dc4c8 100644 --- a/configs/res2net/README.md +++ b/configs/res2net/README.md @@ -2,10 +2,7 @@ > [Res2Net: A New Multi-scale Backbone Architecture](https://arxiv.org/abs/1904.01169) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -23,35 +20,12 @@ state-of-the-art baseline methods such as ResNet-50, DLA-60 and etc. Figure 1. Architecture of Res2Net [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------ | -| res2net50 | 25.76 | 8 | 32 | 224x224 | O2 | 174s | 39.6 | 6464.65 | 79.33 | 94.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/res2net/res2net_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/res2net/res2net50-aa758355-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- | -| res2net50 | 25.76 | 8 | 32 | 224x224 | O2 | 119s | 39.68 | 6451.61 | 79.35 | 94.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/res2net/res2net_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/res2net/res2net50-f42cf71b.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -105,6 +79,35 @@ with `--ckpt_path`. python validate.py -c configs/res2net/res2net_50_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------ | +| res2net50 | 25.76 | 8 | 32 | 224x224 | O2 | 174s | 39.6 | 6464.65 | 79.33 | 94.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/res2net/res2net_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/res2net/res2net50-aa758355-910v2.ckpt) | + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- | +| res2net50 | 25.76 | 8 | 32 | 224x224 | O2 | 119s | 39.68 | 6451.61 | 79.35 | 94.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/res2net/res2net_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/res2net/res2net50-f42cf71b.ckpt) | + + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/resnest/README.md b/configs/resnest/README.md index 8072ee3a3..d05a4df0a 100644 --- a/configs/resnest/README.md +++ b/configs/resnest/README.md @@ -2,10 +2,7 @@ > [ResNeSt: Split-Attention Networks](https://arxiv.org/abs/2004.08955) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -22,28 +19,12 @@ classification.[[1](#references)] Figure 1. Architecture of ResNeSt [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -*coming soon* - -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- | -| resnest50 | 27.55 | 8 | 128 | 224x224 | O2 | 83s | 244.92 | 4552.73 | 80.81 | 95.16 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnest/resnest50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnest/resnest50-f2e7fc9c.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -97,6 +78,28 @@ with `--ckpt_path`. python validate.py -c configs/resnest/resnest50_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +*coming soon* + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- | +| resnest50 | 27.55 | 8 | 128 | 224x224 | O2 | 83s | 244.92 | 4552.73 | 80.81 | 95.16 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnest/resnest50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnest/resnest50-f2e7fc9c.ckpt) | + + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/resnet/README.md b/configs/resnet/README.md index 258c45688..8f2e02550 100644 --- a/configs/resnet/README.md +++ b/configs/resnet/README.md @@ -2,10 +2,7 @@ > [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -21,35 +18,12 @@ networks are easier to optimize, and can gain accuracy from considerably increas Figure 1. Architecture of ResNet [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | -| resnet50 | 25.61 | 8 | 32 | 224x224 | O2 | 77s | 31.9 | 8025.08 | 76.76 | 93.31 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnet/resnet_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnet/resnet50-f369a08d-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | -| resnet50 | 25.61 | 8 | 32 | 224x224 | O2 | 43s | 31.41 | 8150.27 | 76.69 | 93.50 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnet/resnet_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnet/resnet50-e0733ab8.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -103,6 +77,33 @@ with `--ckpt_path`. python validate.py -c configs/resnet/resnet_18_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | +| resnet50 | 25.61 | 8 | 32 | 224x224 | O2 | 77s | 31.9 | 8025.08 | 76.76 | 93.31 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnet/resnet_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnet/resnet50-f369a08d-910v2.ckpt) | + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | +| resnet50 | 25.61 | 8 | 32 | 224x224 | O2 | 43s | 31.41 | 8150.27 | 76.69 | 93.50 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnet/resnet_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnet/resnet50-e0733ab8.ckpt) | + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/resnetv2/README.md b/configs/resnetv2/README.md index 11ae0f9c1..9714980e0 100644 --- a/configs/resnetv2/README.md +++ b/configs/resnetv2/README.md @@ -2,10 +2,7 @@ > [Identity Mappings in Deep Residual Networks](https://arxiv.org/abs/1603.05027) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -20,35 +17,12 @@ to any other block, when using identity mappings as the skip connections and aft Figure 1. Architecture of ResNetV2 [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | -| resnetv2_50 | 25.60 | 8 | 32 | 224x224 | O2 | 120s | 32.19 | 7781.16 | 77.03 | 93.29 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnetv2/resnetv2_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnetv2/resnetv2_50-a0b9f7f8-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | -| resnetv2_50 | 25.60 | 8 | 32 | 224x224 | O2 | 52s | 32.66 | 7838.33 | 76.90 | 93.37 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnetv2/resnetv2_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnetv2/resnetv2_50-3c2f143b.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -102,6 +76,35 @@ with `--ckpt_path`. python validate.py -c configs/resnetv2/resnetv2_50_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | +| resnetv2_50 | 25.60 | 8 | 32 | 224x224 | O2 | 120s | 32.19 | 7781.16 | 77.03 | 93.29 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnetv2/resnetv2_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnetv2/resnetv2_50-a0b9f7f8-910v2.ckpt) | + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | +| resnetv2_50 | 25.60 | 8 | 32 | 224x224 | O2 | 52s | 32.66 | 7838.33 | 76.90 | 93.37 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnetv2/resnetv2_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnetv2/resnetv2_50-3c2f143b.ckpt) | + + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/resnext/README.md b/configs/resnext/README.md index 07702f26f..3596d96a3 100644 --- a/configs/resnext/README.md +++ b/configs/resnext/README.md @@ -2,10 +2,7 @@ > [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -24,35 +21,12 @@ accuracy.[[1](#references)] Figure 1. Architecture of ResNeXt [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | -| resnext50_32x4d | 25.10 | 8 | 32 | 224x224 | O2 | 156s | 44.61 | 5738.62 | 78.64 | 94.18 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnext/resnext50_32x4d_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnext/resnext50_32x4d-988f75bc-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | -| resnext50_32x4d | 25.10 | 8 | 32 | 224x224 | O2 | 49s | 37.22 | 6878.02 | 78.53 | 94.10 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnext/resnext50_32x4d_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnext/resnext50_32x4d-af8aba16.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -106,6 +80,35 @@ with `--ckpt_path`. python validate.py -c configs/resnext/resnext50_32x4d_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | +| resnext50_32x4d | 25.10 | 8 | 32 | 224x224 | O2 | 156s | 44.61 | 5738.62 | 78.64 | 94.18 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnext/resnext50_32x4d_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnext/resnext50_32x4d-988f75bc-910v2.ckpt) | + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | +| resnext50_32x4d | 25.10 | 8 | 32 | 224x224 | O2 | 49s | 37.22 | 6878.02 | 78.53 | 94.10 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnext/resnext50_32x4d_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnext/resnext50_32x4d-af8aba16.ckpt) | + + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/rexnet/README.md b/configs/rexnet/README.md index c6e289ad0..149b15867 100644 --- a/configs/rexnet/README.md +++ b/configs/rexnet/README.md @@ -2,10 +2,7 @@ > [ReXNet: Rethinking Channel Dimensions for Efficient Model Design](https://arxiv.org/abs/2007.00992) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -15,35 +12,11 @@ configuration that can be parameterized by a linear function of the block index lightweight models including NAS-based models and further showed remarkable fine-tuning performances on COCO object detection, instance segmentation, and fine-grained classifications. -## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | -| rexnet_09 | 4.13 | 8 | 64 | 224x224 | O2 | 515s | 115.61 | 3290.28 | 76.14 | 92.96 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/rexnet/rexnet_x09_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/rexnet/rexnet_09-00223eb4-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | -| rexnet_09 | 4.13 | 8 | 64 | 224x224 | O2 | 462s | 130.10 | 3935.43 | 77.06 | 93.41 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/rexnet/rexnet_x09_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/rexnet/rexnet_09-da498331.ckpt) | - -
- -#### Notes +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -97,6 +70,21 @@ with `--ckpt_path`. python validate.py -c configs/rexnet/rexnet_x09_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +*coming soon* + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +*coming soon* + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/senet/README.md b/configs/senet/README.md index 738cc1c1e..e4e1b7951 100644 --- a/configs/senet/README.md +++ b/configs/senet/README.md @@ -2,10 +2,7 @@ > [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -23,35 +20,12 @@ additional computational cost.[[1](#references)] Figure 1. Architecture of SENet [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | -| seresnet18 | 11.80 | 8 | 64 | 224x224 | O2 | 90s | 51.09 | 10021.53 | 72.05 | 90.59 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/senet/seresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/senet/seresnet18-7b971c78-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | -| seresnet18 | 11.80 | 8 | 64 | 224x224 | O2 | 43s | 44.40 | 11531.53 | 71.81 | 90.49 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/senet/seresnet18_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/senet/seresnet18-7880643b.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -105,6 +79,35 @@ with `--ckpt_path`. python validate.py -c configs/senet/seresnet50_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | +| seresnet18 | 11.80 | 8 | 64 | 224x224 | O2 | 90s | 51.09 | 10021.53 | 72.05 | 90.59 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/senet/seresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/senet/seresnet18-7b971c78-910v2.ckpt) | + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | +| seresnet18 | 11.80 | 8 | 64 | 224x224 | O2 | 43s | 44.40 | 11531.53 | 71.81 | 90.49 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/senet/seresnet18_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/senet/seresnet18-7880643b.ckpt) | + + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/shufflenetv1/README.md b/configs/shufflenetv1/README.md index f965e5d97..32e282e47 100644 --- a/configs/shufflenetv1/README.md +++ b/configs/shufflenetv1/README.md @@ -2,10 +2,7 @@ > [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://arxiv.org/abs/1707.01083) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -22,35 +19,12 @@ migrating a large trained model. Figure 1. Architecture of ShuffleNetV1 [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------- | -| shufflenet_v1_g3_05 | 0.73 | 8 | 64 | 224x224 | O2 | 191s | 47.77 | 10718.02 | 57.08 | 79.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv1/shufflenet_v1_g3_05-56209ef3-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------ | -| shufflenet_v1_g3_05 | 0.73 | 8 | 64 | 224x224 | O2 | 169s | 40.62 | 12604.63 | 57.05 | 79.73 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/shufflenet/shufflenetv1/shufflenet_v1_g3_05-42cfe109.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -104,6 +78,35 @@ with `--ckpt_path`. python validate.py -c configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------- | +| shufflenet_v1_g3_05 | 0.73 | 8 | 64 | 224x224 | O2 | 191s | 47.77 | 10718.02 | 57.08 | 79.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv1/shufflenet_v1_g3_05-56209ef3-910v2.ckpt) | + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------ | +| shufflenet_v1_g3_05 | 0.73 | 8 | 64 | 224x224 | O2 | 169s | 40.62 | 12604.63 | 57.05 | 79.73 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/shufflenet/shufflenetv1/shufflenet_v1_g3_05-42cfe109.ckpt) | + + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/shufflenetv2/README.md b/configs/shufflenetv2/README.md index 574021323..d493067e1 100644 --- a/configs/shufflenetv2/README.md +++ b/configs/shufflenetv2/README.md @@ -2,10 +2,7 @@ > [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://arxiv.org/abs/1807.11164) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -29,39 +26,12 @@ Therefore, based on these two principles, ShuffleNetV2 proposes four effective n Figure 1. Architecture Design in ShuffleNetV2 [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------- | -| shufflenet_v2_x0_5 | 1.37 | 8 | 64 | 224x224 | O2 | 100s | 47.32 | 10819.95 | 60.65 | 82.26 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv2/shufflenet_v2_x0_5-39d05bb6-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------- | -| shufflenet_v2_x0_5 | 1.37 | 8 | 64 | 224x224 | O2 | 62s | 41.87 | 12228.33 | 60.53 | 82.11 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/shufflenet/shufflenetv2/shufflenet_v2_x0_5-8c841061.ckpt) | - -
- -#### Notes - -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- All models are trained on ImageNet-1K training set and the top-1 accuracy is reported on the validatoin set. ## Quick Start @@ -115,6 +85,36 @@ with `--ckpt_path`. python validate.py -c configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------- | +| shufflenet_v2_x0_5 | 1.37 | 8 | 64 | 224x224 | O2 | 100s | 47.32 | 10819.95 | 60.65 | 82.26 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv2/shufflenet_v2_x0_5-39d05bb6-910v2.ckpt) | + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------- | +| shufflenet_v2_x0_5 | 1.37 | 8 | 64 | 224x224 | O2 | 62s | 41.87 | 12228.33 | 60.53 | 82.11 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/shufflenet/shufflenetv2/shufflenet_v2_x0_5-8c841061.ckpt) | + + + +### Notes + +- All models are trained on ImageNet-1K training set and the top-1 accuracy is reported on the validatoin set. +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/sknet/README.md b/configs/sknet/README.md index c90f9d889..389049f13 100644 --- a/configs/sknet/README.md +++ b/configs/sknet/README.md @@ -2,10 +2,7 @@ > [Selective Kernel Networks](https://arxiv.org/pdf/1903.06586) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -27,35 +24,12 @@ multi-scale information from, e.g., 3×3, 5×5, 7×7 convolutional kernels insid Figure 1. Selective Kernel Convolution.

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | -| skresnet18 | 11.97 | 8 | 64 | 224x224 | O2 | 134s | 49.83 | 10274.93 | 72.85 | 90.83 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/sknet/skresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/sknet/skresnet18-9d8b1afc-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | -| skresnet18 | 11.97 | 8 | 64 | 224x224 | O2 | 60s | 45.84 | 11169.28 | 73.09 | 91.20 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/sknet/skresnet18_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/sknet/skresnet18-868228e5.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -109,6 +83,36 @@ with `--ckpt_path`. python validate.py -c configs/sknet/skresnext50_32x4d_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | +| skresnet18 | 11.97 | 8 | 64 | 224x224 | O2 | 134s | 49.83 | 10274.93 | 72.85 | 90.83 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/sknet/skresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/sknet/skresnet18-9d8b1afc-910v2.ckpt) | + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | +| skresnet18 | 11.97 | 8 | 64 | 224x224 | O2 | 60s | 45.84 | 11169.28 | 73.09 | 91.20 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/sknet/skresnet18_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/sknet/skresnet18-868228e5.ckpt) | + + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/squeezenet/README.md b/configs/squeezenet/README.md index 6995d411c..3ab4ffb41 100644 --- a/configs/squeezenet/README.md +++ b/configs/squeezenet/README.md @@ -2,10 +2,7 @@ > [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://arxiv.org/abs/1602.07360) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -24,35 +21,12 @@ Middle: SqueezeNet with simple bypass; Right: SqueezeNet with complex bypass. Figure 1. Architecture of SqueezeNet [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | -| squeezenet1_0 | 1.25 | 8 | 32 | 224x224 | O2 | 64s | 23.48 | 10902.90 | 58.75 | 80.76 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/squeezenet/squeezenet_1.0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/squeezenet/squeezenet1_0-24010b28-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | -| squeezenet1_0 | 1.25 | 8 | 32 | 224x224 | O2 | 45s | 22.36 | 11449.02 | 58.67 | 80.61 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/squeezenet/squeezenet_1.0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/squeezenet/squeezenet1_0-eb911778.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -106,6 +80,35 @@ with `--ckpt_path`. python validate.py -c configs/squeezenet/squeezenet_1.0_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | +| squeezenet1_0 | 1.25 | 8 | 32 | 224x224 | O2 | 64s | 23.48 | 10902.90 | 58.75 | 80.76 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/squeezenet/squeezenet_1.0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/squeezenet/squeezenet1_0-24010b28-910v2.ckpt) | + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | +| squeezenet1_0 | 1.25 | 8 | 32 | 224x224 | O2 | 45s | 22.36 | 11449.02 | 58.67 | 80.61 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/squeezenet/squeezenet_1.0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/squeezenet/squeezenet1_0-eb911778.ckpt) | + + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/swintransformer/README.md b/configs/swintransformer/README.md index 85362aa94..910d4d210 100644 --- a/configs/swintransformer/README.md +++ b/configs/swintransformer/README.md @@ -3,10 +3,7 @@ > [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -30,44 +27,12 @@ on ImageNet-1K dataset compared with ViT and ResNet.[[1](#references)] Figure 1. Architecture of Swin Transformer [1]

-## Performance - - - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- | -| swin_tiny | 33.38 | 8 | 256 | 224x224 | O2 | 266s | 466.6 | 4389.20 | 80.90 | 94.90 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformer/swin_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swin/swin_tiny-72b3c5e6-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | -| swin_tiny | 33.38 | 8 | 256 | 224x224 | O2 | 226s | 454.49 | 4506.15 | 80.82 | 94.80 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformer/swin_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/swin/swin_tiny-0ff2f96d.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -122,6 +87,36 @@ with `--ckpt_path`. python validate.py -c configs/swintransformer/swin_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- | +| swin_tiny | 33.38 | 8 | 256 | 224x224 | O2 | 266s | 466.6 | 4389.20 | 80.90 | 94.90 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformer/swin_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swin/swin_tiny-72b3c5e6-910v2.ckpt) | + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | +| swin_tiny | 33.38 | 8 | 256 | 224x224 | O2 | 226s | 454.49 | 4506.15 | 80.82 | 94.80 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformer/swin_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/swin/swin_tiny-0ff2f96d.ckpt) | + + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/swintransformerv2/README.md b/configs/swintransformerv2/README.md index c92f448c4..672911eb3 100644 --- a/configs/swintransformerv2/README.md +++ b/configs/swintransformerv2/README.md @@ -2,10 +2,7 @@ > [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -25,35 +22,12 @@ semantic segmentation, and Kinetics-400 video action classification.[[1](#refere Figure 1. Architecture of Swin Transformer V2 [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | -| swinv2_tiny_window8 | 28.78 | 8 | 128 | 256x256 | O2 | 385s | 335.18 | 3055.07 | 81.38 | 95.46 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swinv2/swinv2_tiny_window8-70c5e903-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | -| swinv2_tiny_window8 | 28.78 | 8 | 128 | 256x256 | O2 | 273s | 317.19 | 3228.35 | 81.42 | 95.43 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/swinv2/swinv2_tiny_window8-3ef8b787.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -107,6 +81,35 @@ with `--ckpt_path`. python validate.py -c configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | +| swinv2_tiny_window8 | 28.78 | 8 | 128 | 256x256 | O2 | 385s | 335.18 | 3055.07 | 81.38 | 95.46 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swinv2/swinv2_tiny_window8-70c5e903-910v2.ckpt) | + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ------------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | +| swinv2_tiny_window8 | 28.78 | 8 | 128 | 256x256 | O2 | 273s | 317.19 | 3228.35 | 81.42 | 95.43 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/swinv2/swinv2_tiny_window8-3ef8b787.ckpt) | + + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/vgg/README.md b/configs/vgg/README.md index fe7c155a6..190e010ee 100644 --- a/configs/vgg/README.md +++ b/configs/vgg/README.md @@ -3,10 +3,7 @@ > [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -26,46 +23,12 @@ methods such as GoogleLeNet and AlexNet on ImageNet-1K dataset.[[1](#references) Figure 1. Architecture of VGG [1]

-## Performance - - - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | -| vgg13 | 133.04 | 8 | 32 | 224x224 | O2 | 41s | 30.52 | 8387.94 | 72.81 | 91.02 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg13-7756f33c-910v2.ckpt) | -| vgg19 | 143.66 | 8 | 32 | 224x224 | O2 | 53s | 39.17 | 6535.61 | 75.24 | 92.55 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg19-5104d1ea-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | -| vgg13 | 133.04 | 8 | 32 | 224x224 | O2 | 23s | 55.20 | 4637.68 | 72.87 | 91.02 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg13-da805e6e.ckpt) | -| vgg19 | 143.66 | 8 | 32 | 224x224 | O2 | 22s | 67.42 | 3797.09 | 75.21 | 92.56 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg19-bedee7b6.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -119,6 +82,37 @@ with `--ckpt_path`. python validate.py -c configs/vgg/vgg16_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | +| vgg13 | 133.04 | 8 | 32 | 224x224 | O2 | 41s | 30.52 | 8387.94 | 72.81 | 91.02 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg13-7756f33c-910v2.ckpt) | +| vgg19 | 143.66 | 8 | 32 | 224x224 | O2 | 53s | 39.17 | 6535.61 | 75.24 | 92.55 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg19-5104d1ea-910v2.ckpt) | + + + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | +| vgg13 | 133.04 | 8 | 32 | 224x224 | O2 | 23s | 55.20 | 4637.68 | 72.87 | 91.02 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg13-da805e6e.ckpt) | +| vgg19 | 143.66 | 8 | 32 | 224x224 | O2 | 22s | 67.42 | 3797.09 | 75.21 | 92.56 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg19-bedee7b6.ckpt) | + + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/visformer/README.md b/configs/visformer/README.md index 532556be5..241b3df15 100644 --- a/configs/visformer/README.md +++ b/configs/visformer/README.md @@ -2,10 +2,7 @@ > [Visformer: The Vision-friendly Transformer](https://arxiv.org/abs/2104.12533) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -23,37 +20,12 @@ BatchNorm to patch embedding modules as in CNNs. [[2](#references)] Figure 1. Network Configuration of Visformer [1]

-## Performance - -## ImageNet-1k - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------- | -| visformer_tiny | 10.33 | 8 | 128 | 224x224 | O2 | 169s | 201.14 | 5090.98 | 78.40 | 94.30 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/visformer/visformer_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/visformer/visformer_tiny-df995ba4-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------- | -| visformer_tiny | 10.33 | 8 | 128 | 224x224 | O2 | 137s | 217.92 | 4698.97 | 78.28 | 94.15 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/visformer/visformer_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/visformer/visformer_tiny-daee0322.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -105,6 +77,27 @@ with `--ckpt_path`. python validate.py -c configs/visformer/visformer_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +*coming soon* + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + + +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------- | +| visformer_tiny | 10.33 | 8 | 128 | 224x224 | O2 | 137s | 217.92 | 4698.97 | 78.28 | 94.15 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/visformer/visformer_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/visformer/visformer_tiny-daee0322.ckpt) | + + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/vit/README.md b/configs/vit/README.md index b87ce7773..dd1002cb7 100644 --- a/configs/vit/README.md +++ b/configs/vit/README.md @@ -4,10 +4,7 @@ > [ An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -33,30 +30,12 @@ fewer computational resources. [[2](#references)] Figure 1. Architecture of ViT [1]

-## Performance - - - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -*coming soon* - -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -*coming soon* +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -117,6 +96,21 @@ with `--ckpt_path`. python validate.py -c configs/vit/vit_b32_224_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +*coming soon* + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +*coming soon* + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/configs/volo/README.md b/configs/volo/README.md index 435761a6e..b0799b88d 100644 --- a/configs/volo/README.md +++ b/configs/volo/README.md @@ -2,10 +2,7 @@ > [VOLO: Vision Outlooker for Visual Recognition ](https://arxiv.org/abs/2106.13112) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -24,27 +21,12 @@ without using any extra training data. Figure 1. Illustration of outlook attention. [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -performance tested on ascend 910*(8p) with graph mode - -*coming soon* - -performance tested on ascend 910(8p) with graph mode - -
- -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------- | -| volo_d1 | 27 | 8 | 128 | 224x224 | O2 | 275s | 270.79 | 3781.53 | 82.59 | 95.99 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/visformer/visformer_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/visformer/visformer_tiny-df995ba4-910v2.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -98,6 +80,22 @@ with `--ckpt_path`. python validate.py -c configs/volo/volo_d1_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +performance tested on ascend 910*(8p) with graph mode + +*coming soon* + +performance tested on ascend 910(8p) with graph mode + +*coming soon* + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/xception/README.md b/configs/xception/README.md index 2e5a2ded2..3fa097b43 100644 --- a/configs/xception/README.md +++ b/configs/xception/README.md @@ -2,10 +2,7 @@ > [Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/pdf/1610.02357.pdf) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -25,28 +22,12 @@ module.[[1](#references)] Figure 1. Architecture of Xception [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -*coming soon* - -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | -| xception | 22.91 | 8 | 32 | 299x299 | O2 | 161s | 96.78 | 2645.17 | 79.01 | 94.25 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/xception/xception_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/xception/xception-2c1e711df.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -100,6 +81,23 @@ with `--ckpt_path`. python validate.py -c configs/xception/xception_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +*coming soon* + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +*coming soon* + + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. + ## References diff --git a/configs/xcit/README.md b/configs/xcit/README.md index a42647e84..12e154b33 100644 --- a/configs/xcit/README.md +++ b/configs/xcit/README.md @@ -2,10 +2,7 @@ > [XCiT: Cross-Covariance Image Transformers](https://arxiv.org/abs/2106.09681) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Introduction @@ -22,35 +19,12 @@ transformers with the scalability of convolutional architectures. Figure 1. Architecture of XCiT [1]

-## Performance - -Our reproduced model performance on ImageNet-1K is reported as follows. - -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| -------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | -| xcit_tiny_12_p16_224 | 7.00 | 8 | 128 | 224x224 | O2 | 330s | 229.25 | 4466.74 | 77.27 | 93.56 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/xcit/xcit_tiny_12_p16_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/xcit/xcit_tiny_12_p16_224-bd90776e-910v2.ckpt) | - -
- -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode - -
- - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| -------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------ | -| xcit_tiny_12_p16_224 | 7.00 | 8 | 128 | 224x224 | O2 | 382s | 252.98 | 4047.75 | 77.67 | 93.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/xcit/xcit_tiny_12_p16_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/xcit/xcit_tiny_12_p16_224-1b1c9301.ckpt) | - -
+## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | -#### Notes -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start @@ -101,6 +75,21 @@ with `--ckpt_path`. ``` python validate.py -c configs/xcit/xcit_tiny_12_p16_224_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` +## Performance + +Our reproduced model performance on ImageNet-1K is reported as follows. + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. + +*coming soon* + +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + +*coming soon* + +### Notes + +- top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. ## References diff --git a/examples/det/ssd/README.md b/examples/det/ssd/README.md index fa98492fd..c0dac5dfe 100644 --- a/examples/det/ssd/README.md +++ b/examples/det/ssd/README.md @@ -2,10 +2,6 @@ > [SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Introduction @@ -20,6 +16,11 @@ SSD is an single-staged object detector. It discretizes the output space of boun In this example, by leveraging [the multi-scale feature extraction of MindCV](https://github.com/mindspore-lab/mindcv/blob/main/docs/en/how_to_guides/feature_extraction.md), we demonstrate that using backbones from MindCV much simplifies the implementation of SSD. +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Configurations Here, we provide three configurations of SSD. @@ -68,7 +69,7 @@ Specify the path of the preprocessed dataset at keyword `data_dir` in the config |:----------------:|:----------------:|:----------------:| | [backbone weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_100-d5532038.ckpt) | [backbone weights](https://download.mindspore.cn/toolkits/mindcv/resnet/resnet50-e0733ab8.ckpt) | [backbone weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-1279ad5f.ckpt) | -
+ ### Train @@ -130,28 +131,22 @@ cd mindcv # change directory to the root of MindCV repository python examples/det/ssd/eval.py --config examples/det/ssd/ssd_mobilenetv2.yaml ``` -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Performance -- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. *coming soon* -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + -
| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | mAP | recipe | weight | -| ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | ---- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------- | +| ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | ---- | ------------------------------------------------------------------------------------------------ |---------------------------------------------------------------------------------------------| | ssd_mobilenetv2 | 4.45 | 8 | 32 | 300x300 | O2 | 202s | 60.14 | 4256.73 | 23.2 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/examples/det/ssd/ssd_mobilenetv2.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ssd/ssd_mobilenetv2-5bbd7411.ckpt) | | ssd_resnet50_fpn | 33.37 | 8 | 32 | 640x640 | O2 | 130s | 269.82 | 948.78 | 38.3 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/examples/det/ssd/ssd_resnet50_fpn.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ssd/ssd_resnet50_fpn-ac87ddac.ckpt) | -| ssd_mobilenetv3 | 4.88 | 8 | 32 | 300x300 | O2 | 245s | 59.91 | 4273.08 | 23.8 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/examples/det/ssd/ssd_mobilenetv3.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ssd/ssd_mobilenetv3-53d9f6e9.ckpt) | -
## References diff --git a/examples/seg/deeplabv3/README.md b/examples/seg/deeplabv3/README.md index 723880a05..437a13525 100644 --- a/examples/seg/deeplabv3/README.md +++ b/examples/seg/deeplabv3/README.md @@ -4,10 +4,6 @@ > > DeeplabV3+:[Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611) -## Requirements -| mindspore | ascend driver | firmware | cann toolkit/kernel | -| :-------: | :-----------: | :---------: | :-----------------: | -| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | ## Introduction @@ -34,6 +30,11 @@ This example provides implementations of DeepLabV3 and DeepLabV3+ using backbones from MindCV. More details about feature extraction of MindCV are in [this tutorial](https://github.com/mindspore-lab/mindcv/blob/main/docs/en/how_to_guides/feature_extraction.md). Note that the ResNet in DeepLab contains atrous convolutions with different rates, `dilated_resnet.py` is provided as a modification of ResNet from MindCV, with atrous convolutions in block 3-4. +## Requirements +| mindspore | ascend driver | firmware | cann toolkit/kernel | +| :-------: | :-----------: | :---------: | :-----------------: | +| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 | + ## Quick Start ### Preparation @@ -148,9 +149,9 @@ python examples/seg/deeplabv3/eval.py --config examples/seg/deeplabv3/config/dee ``` ## Performance -- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode +Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. + -
| model name | params(M) | cards | batch size | jit level | graph compile | ms/step | img/s | mIoU | recipe | weight | | ----------------- | --------- | ----- | ---------- | --------- | ------------- | ------- | ------ | ------------------- | -------------------------------------------------------------------------------------------------------------------------------- | ----------- | @@ -159,9 +160,9 @@ python examples/seg/deeplabv3/eval.py --config examples/seg/deeplabv3/config/dee | deeplabv3plus_s16 | 59.45 | 8 | 32 | O2 | 207s | 312.15 | 820.12 | 78.99 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/examples/seg/deeplabv3/config/deeplabv3plus_s16_dilated_resnet101.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/deeplabv3/deeplabv3plus-s16-best.ckpt) | | deeplabv3plus_s8 | 59.45 | 8 | 16 | O2 | 170s | 403.43 | 217.28 | 80.31\|80.99\|81.10 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/examples/seg/deeplabv3/config/deeplabv3plus_s8_dilated_resnet101.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/deeplabv3/deeplabv3plus-s8-best.ckpt) | -
-- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode + +Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode. *coming soon*