简体ä¸ć–‡ | English
PaddleClas is a toolset for image classification tasks prepared for the industry and academia. It helps users train better computer vision models and apply them in real scenarios.
Recent update
- 2020.10.12 Add Paddle-Lite demo。
- 2020.10.10 Add cpp inference demo and improve FAQ tutorial.
- 2020.09.17 Add
HRNet_W48_C_ssld
pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.62%. AddResNet34_vd_ssld
pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 79.72%. - 2020.09.07 Add
HRNet_W18_C_ssld
pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 81.16%. - 2020.07.14 Add
Res2Net200_vd_26w_4s_ssld
pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 85.13%. AddFix_ResNet50_vd_ssld_v2
pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 84.00%. - 2020.06.17 Add English documents.
- 2020.06.12 Add support for training and evaluation on Windows or CPU.
- more
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Rich model zoo. Based on the ImageNet-1k classification dataset, PaddleClas provides 24 series of classification network structures and training configurations, 122 models' pretrained weights and their evaluation metrics.
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SSLD Knowledge Distillation. Based on this SSLD distillation strategy, the top-1 acc of the distilled model is generally increased by more than 3%.
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Data augmentation: PaddleClas provides detailed introduction of 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, code reproduction and effect evaluation in a unified experimental environment.
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Pretrained model with 100,000 categories: Based on
ResNet50_vd
model, Baidu open sourced theResNet50_vd
pretrained model trained on a 100,000-category dataset. In some practical scenarios, the accuracy based on the pretrained weights can be increased by up to 30%. -
A variety of training modes, including multi-machine training, mixed precision training, etc.
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A variety of inference and deployment solutions, including TensorRT inference, Paddle-Lite inference, model service deployment, model quantification, Paddle Hub, etc.
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Support Linux, Windows, macOS and other systems.
- Installation
- Quick start PaddleClas in 30 minutes
- Model introduction and model zoo
- Model training/evaluation
- Model prediction/inference
- Advanced tutorials
- Applications
- FAQ
- Competition support
- License
- Contribution
Based on the ImageNet-1k classification dataset, the 24 classification network structures supported by PaddleClas and the corresponding 122 image classification pretrained models are shown below. Training trick, a brief introduction to each series of network structures, and performance evaluation will be shown in the corresponding chapters. The evaluation environment is as follows.
- CPU evaluation environment is based on Snapdragon 855 (SD855).
- The GPU evaluation speed is measured by running 500 times under the FP32+TensorRT configuration (excluding the warmup time of the first 10 times).
Curves of accuracy to the inference time of common server-side models are shown as follows.
Curves of accuracy to the inference time and storage size of common mobile-side models are shown as follows.
Accuracy and inference time metrics of ResNet and Vd series models are shown as follows. More detailed information can be refered to ResNet and Vd series tutorial.
Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
Flops(G) | Params(M) | Download Address |
---|---|---|---|---|---|---|---|
ResNet18 | 0.7098 | 0.8992 | 1.45606 | 3.56305 | 3.66 | 11.69 | Download link |
ResNet18_vd | 0.7226 | 0.9080 | 1.54557 | 3.85363 | 4.14 | 11.71 | Download link |
ResNet34 | 0.7457 | 0.9214 | 2.34957 | 5.89821 | 7.36 | 21.8 | Download link |
ResNet34_vd | 0.7598 | 0.9298 | 2.43427 | 6.22257 | 7.39 | 21.82 | Download link |
ResNet34_vd_ssld | 0.7972 | 0.9490 | 2.43427 | 6.22257 | 7.39 | 21.82 | Download link |
ResNet50 | 0.7650 | 0.9300 | 3.47712 | 7.84421 | 8.19 | 25.56 | Download link |
ResNet50_vc | 0.7835 | 0.9403 | 3.52346 | 8.10725 | 8.67 | 25.58 | Download link |
ResNet50_vd | 0.7912 | 0.9444 | 3.53131 | 8.09057 | 8.67 | 25.58 | Download link |
ResNet50_vd_v2 | 0.7984 | 0.9493 | 3.53131 | 8.09057 | 8.67 | 25.58 | Download link |
ResNet101 | 0.7756 | 0.9364 | 6.07125 | 13.40573 | 15.52 | 44.55 | Download link |
ResNet101_vd | 0.8017 | 0.9497 | 6.11704 | 13.76222 | 16.1 | 44.57 | Download link |
ResNet152 | 0.7826 | 0.9396 | 8.50198 | 19.17073 | 23.05 | 60.19 | Download link |
ResNet152_vd | 0.8059 | 0.9530 | 8.54376 | 19.52157 | 23.53 | 60.21 | Download link |
ResNet200_vd | 0.8093 | 0.9533 | 10.80619 | 25.01731 | 30.53 | 74.74 | Download link |
ResNet50_vd_ ssld |
0.8239 | 0.9610 | 3.53131 | 8.09057 | 8.67 | 25.58 | Download link |
ResNet50_vd_ ssld_v2 |
0.8300 | 0.9640 | 3.53131 | 8.09057 | 8.67 | 25.58 | Download link |
ResNet101_vd_ ssld |
0.8373 | 0.9669 | 6.11704 | 13.76222 | 16.1 | 44.57 | Download link |
Accuracy and inference time metrics of Mobile series models are shown as follows. More detailed information can be refered to Mobile series tutorial.
Model | Top-1 Acc | Top-5 Acc | SD855 time(ms) bs=1 |
Flops(G) | Params(M) | Model storage size(M) | Download Address |
---|---|---|---|---|---|---|---|
MobileNetV1_ x0_25 |
0.5143 | 0.7546 | 3.21985 | 0.07 | 0.46 | 1.9 | Download link |
MobileNetV1_ x0_5 |
0.6352 | 0.8473 | 9.579599 | 0.28 | 1.31 | 5.2 | Download link |
MobileNetV1_ x0_75 |
0.6881 | 0.8823 | 19.436399 | 0.63 | 2.55 | 10 | Download link |
MobileNetV1 | 0.7099 | 0.8968 | 32.523048 | 1.11 | 4.19 | 16 | Download link |
MobileNetV1_ ssld |
0.7789 | 0.9394 | 32.523048 | 1.11 | 4.19 | 16 | Download link |
MobileNetV2_ x0_25 |
0.5321 | 0.7652 | 3.79925 | 0.05 | 1.5 | 6.1 | Download link |
MobileNetV2_ x0_5 |
0.6503 | 0.8572 | 8.7021 | 0.17 | 1.93 | 7.8 | Download link |
MobileNetV2_ x0_75 |
0.6983 | 0.8901 | 15.531351 | 0.35 | 2.58 | 10 | Download link |
MobileNetV2 | 0.7215 | 0.9065 | 23.317699 | 0.6 | 3.44 | 14 | Download link |
MobileNetV2_ x1_5 |
0.7412 | 0.9167 | 45.623848 | 1.32 | 6.76 | 26 | Download link |
MobileNetV2_ x2_0 |
0.7523 | 0.9258 | 74.291649 | 2.32 | 11.13 | 43 | Download link |
MobileNetV2_ ssld |
0.7674 | 0.9339 | 23.317699 | 0.6 | 3.44 | 14 | Download link |
MobileNetV3_ large_x1_25 |
0.7641 | 0.9295 | 28.217701 | 0.714 | 7.44 | 29 | Download link |
MobileNetV3_ large_x1_0 |
0.7532 | 0.9231 | 19.30835 | 0.45 | 5.47 | 21 | Download link |
MobileNetV3_ large_x0_75 |
0.7314 | 0.9108 | 13.5646 | 0.296 | 3.91 | 16 | Download link |
MobileNetV3_ large_x0_5 |
0.6924 | 0.8852 | 7.49315 | 0.138 | 2.67 | 11 | Download link |
MobileNetV3_ large_x0_35 |
0.6432 | 0.8546 | 5.13695 | 0.077 | 2.1 | 8.6 | Download link |
MobileNetV3_ small_x1_25 |
0.7067 | 0.8951 | 9.2745 | 0.195 | 3.62 | 14 | Download link |
MobileNetV3_ small_x1_0 |
0.6824 | 0.8806 | 6.5463 | 0.123 | 2.94 | 12 | Download link |
MobileNetV3_ small_x0_75 |
0.6602 | 0.8633 | 5.28435 | 0.088 | 2.37 | 9.6 | Download link |
MobileNetV3_ small_x0_5 |
0.5921 | 0.8152 | 3.35165 | 0.043 | 1.9 | 7.8 | Download link |
MobileNetV3_ small_x0_35 |
0.5303 | 0.7637 | 2.6352 | 0.026 | 1.66 | 6.9 | Download link |
MobileNetV3_ small_x0_35_ssld |
0.5555 | 0.7771 | 2.6352 | 0.026 | 1.66 | 6.9 | Download link |
MobileNetV3_ large_x1_0_ssld |
0.7896 | 0.9448 | 19.30835 | 0.45 | 5.47 | 21 | Download link |
MobileNetV3_large_ x1_0_ssld_int8 |
0.7605 | - | 14.395 | - | - | 10 | Download link |
MobileNetV3_small_ x1_0_ssld |
0.7129 | 0.9010 | 6.5463 | 0.123 | 2.94 | 12 | Download link |
ShuffleNetV2 | 0.6880 | 0.8845 | 10.941 | 0.28 | 2.26 | 9 | Download link |
ShuffleNetV2_ x0_25 |
0.4990 | 0.7379 | 2.329 | 0.03 | 0.6 | 2.7 | Download link |
ShuffleNetV2_ x0_33 |
0.5373 | 0.7705 | 2.64335 | 0.04 | 0.64 | 2.8 | Download link |
ShuffleNetV2_ x0_5 |
0.6032 | 0.8226 | 4.2613 | 0.08 | 1.36 | 5.6 | Download link |
ShuffleNetV2_ x1_5 |
0.7163 | 0.9015 | 19.3522 | 0.58 | 3.47 | 14 | Download link |
ShuffleNetV2_ x2_0 |
0.7315 | 0.9120 | 34.770149 | 1.12 | 7.32 | 28 | Download link |
ShuffleNetV2_ swish |
0.7003 | 0.8917 | 16.023151 | 0.29 | 2.26 | 9.1 | Download link |
DARTS_GS_4M | 0.7523 | 0.9215 | 47.204948 | 1.04 | 4.77 | 21 | Download link |
DARTS_GS_6M | 0.7603 | 0.9279 | 53.720802 | 1.22 | 5.69 | 24 | Download link |
GhostNet_ x0_5 |
0.6688 | 0.8695 | 5.7143 | 0.082 | 2.6 | 10 | Download link |
GhostNet_ x1_0 |
0.7402 | 0.9165 | 13.5587 | 0.294 | 5.2 | 20 | Download link |
GhostNet_ x1_3 |
0.7579 | 0.9254 | 19.9825 | 0.44 | 7.3 | 29 | Download link |
Accuracy and inference time metrics of SEResNeXt and Res2Net series models are shown as follows. More detailed information can be refered to SEResNext and_Res2Net series tutorial.
Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
Flops(G) | Params(M) | Download Address |
---|---|---|---|---|---|---|---|
Res2Net50_ 26w_4s |
0.7933 | 0.9457 | 4.47188 | 9.65722 | 8.52 | 25.7 | Download link |
Res2Net50_vd_ 26w_4s |
0.7975 | 0.9491 | 4.52712 | 9.93247 | 8.37 | 25.06 | Download link |
Res2Net50_ 14w_8s |
0.7946 | 0.9470 | 5.4026 | 10.60273 | 9.01 | 25.72 | Download link |
Res2Net101_vd_ 26w_4s |
0.8064 | 0.9522 | 8.08729 | 17.31208 | 16.67 | 45.22 | Download link |
Res2Net200_vd_ 26w_4s |
0.8121 | 0.9571 | 14.67806 | 32.35032 | 31.49 | 76.21 | Download link |
Res2Net200_vd_ 26w_4s_ssld |
0.8513 | 0.9742 | 14.67806 | 32.35032 | 31.49 | 76.21 | Download link |
ResNeXt50_ 32x4d |
0.7775 | 0.9382 | 7.56327 | 10.6134 | 8.02 | 23.64 | Download link |
ResNeXt50_vd_ 32x4d |
0.7956 | 0.9462 | 7.62044 | 11.03385 | 8.5 | 23.66 | Download link |
ResNeXt50_ 64x4d |
0.7843 | 0.9413 | 13.80962 | 18.4712 | 15.06 | 42.36 | Download link |
ResNeXt50_vd_ 64x4d |
0.8012 | 0.9486 | 13.94449 | 18.88759 | 15.54 | 42.38 | Download link |
ResNeXt101_ 32x4d |
0.7865 | 0.9419 | 16.21503 | 19.96568 | 15.01 | 41.54 | Download link |
ResNeXt101_vd_ 32x4d |
0.8033 | 0.9512 | 16.28103 | 20.25611 | 15.49 | 41.56 | Download link |
ResNeXt101_ 64x4d |
0.7835 | 0.9452 | 30.4788 | 36.29801 | 29.05 | 78.12 | Download link |
ResNeXt101_vd_ 64x4d |
0.8078 | 0.9520 | 30.40456 | 36.77324 | 29.53 | 78.14 | Download link |
ResNeXt152_ 32x4d |
0.7898 | 0.9433 | 24.86299 | 29.36764 | 22.01 | 56.28 | Download link |
ResNeXt152_vd_ 32x4d |
0.8072 | 0.9520 | 25.03258 | 30.08987 | 22.49 | 56.3 | Download link |
ResNeXt152_ 64x4d |
0.7951 | 0.9471 | 46.7564 | 56.34108 | 43.03 | 107.57 | Download link |
ResNeXt152_vd_ 64x4d |
0.8108 | 0.9534 | 47.18638 | 57.16257 | 43.52 | 107.59 | Download link |
SE_ResNet18_vd | 0.7333 | 0.9138 | 1.7691 | 4.19877 | 4.14 | 11.8 | Download link |
SE_ResNet34_vd | 0.7651 | 0.9320 | 2.88559 | 7.03291 | 7.84 | 21.98 | Download link |
SE_ResNet50_vd | 0.7952 | 0.9475 | 4.28393 | 10.38846 | 8.67 | 28.09 | Download link |
SE_ResNeXt50_ 32x4d |
0.7844 | 0.9396 | 8.74121 | 13.563 | 8.02 | 26.16 | Download link |
SE_ResNeXt50_vd_ 32x4d |
0.8024 | 0.9489 | 9.17134 | 14.76192 | 10.76 | 26.28 | Download link |
SE_ResNeXt101_ 32x4d |
0.7912 | 0.9420 | 18.82604 | 25.31814 | 15.02 | 46.28 | Download link |
SENet154_vd | 0.8140 | 0.9548 | 53.79794 | 66.31684 | 45.83 | 114.29 | Download link |
Accuracy and inference time metrics of DPN and DenseNet series models are shown as follows. More detailed information can be refered to DPN and DenseNet series tutorial.
Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
Flops(G) | Params(M) | Download Address |
---|---|---|---|---|---|---|---|
DenseNet121 | 0.7566 | 0.9258 | 4.40447 | 9.32623 | 5.69 | 7.98 | Download link |
DenseNet161 | 0.7857 | 0.9414 | 10.39152 | 22.15555 | 15.49 | 28.68 | Download link |
DenseNet169 | 0.7681 | 0.9331 | 6.43598 | 12.98832 | 6.74 | 14.15 | Download link |
DenseNet201 | 0.7763 | 0.9366 | 8.20652 | 17.45838 | 8.61 | 20.01 | Download link |
DenseNet264 | 0.7796 | 0.9385 | 12.14722 | 26.27707 | 11.54 | 33.37 | Download link |
DPN68 | 0.7678 | 0.9343 | 11.64915 | 12.82807 | 4.03 | 10.78 | Download link |
DPN92 | 0.7985 | 0.9480 | 18.15746 | 23.87545 | 12.54 | 36.29 | Download link |
DPN98 | 0.8059 | 0.9510 | 21.18196 | 33.23925 | 22.22 | 58.46 | Download link |
DPN107 | 0.8089 | 0.9532 | 27.62046 | 52.65353 | 35.06 | 82.97 | Download link |
DPN131 | 0.8070 | 0.9514 | 28.33119 | 46.19439 | 30.51 | 75.36 | Download link |
Accuracy and inference time metrics of HRNet series models are shown as follows. More detailed information can be refered to Mobile series tutorial.
Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
Flops(G) | Params(M) | Download Address |
---|---|---|---|---|---|---|---|
HRNet_W18_C | 0.7692 | 0.9339 | 7.40636 | 13.29752 | 4.14 | 21.29 | Download link |
HRNet_W18_C_ssld | 0.81162 | 0.95804 | 7.40636 | 13.29752 | 4.14 | 21.29 | Download link |
HRNet_W30_C | 0.7804 | 0.9402 | 9.57594 | 17.35485 | 16.23 | 37.71 | Download link |
HRNet_W32_C | 0.7828 | 0.9424 | 9.49807 | 17.72921 | 17.86 | 41.23 | Download link |
HRNet_W40_C | 0.7877 | 0.9447 | 12.12202 | 25.68184 | 25.41 | 57.55 | Download link |
HRNet_W44_C | 0.7900 | 0.9451 | 13.19858 | 32.25202 | 29.79 | 67.06 | Download link |
HRNet_W48_C | 0.7895 | 0.9442 | 13.70761 | 34.43572 | 34.58 | 77.47 | Download link |
HRNet_W48_C_ssld | 0.8363 | 0.9682 | 13.70761 | 34.43572 | 34.58 | 77.47 | Download link |
HRNet_W64_C | 0.7930 | 0.9461 | 17.57527 | 47.9533 | 57.83 | 128.06 | Download link |
Accuracy and inference time metrics of Inception series models are shown as follows. More detailed information can be refered to Inception series tutorial.
Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
Flops(G) | Params(M) | Download Address |
---|---|---|---|---|---|---|---|
GoogLeNet | 0.7070 | 0.8966 | 1.88038 | 4.48882 | 2.88 | 8.46 | Download link |
Xception41 | 0.7930 | 0.9453 | 4.96939 | 17.01361 | 16.74 | 22.69 | Download link |
Xception41_deeplab | 0.7955 | 0.9438 | 5.33541 | 17.55938 | 18.16 | 26.73 | Download link |
Xception65 | 0.8100 | 0.9549 | 7.26158 | 25.88778 | 25.95 | 35.48 | Download link |
Xception65_deeplab | 0.8032 | 0.9449 | 7.60208 | 26.03699 | 27.37 | 39.52 | Download link |
Xception71 | 0.8111 | 0.9545 | 8.72457 | 31.55549 | 31.77 | 37.28 | Download link |
InceptionV4 | 0.8077 | 0.9526 | 12.99342 | 25.23416 | 24.57 | 42.68 | Download link |
Accuracy and inference time metrics of EfficientNet and ResNeXt101_wsl series models are shown as follows. More detailed information can be refered to EfficientNet and ResNeXt101_wsl series tutorial.
Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
Flops(G) | Params(M) | Download Address |
---|---|---|---|---|---|---|---|
ResNeXt101_ 32x8d_wsl |
0.8255 | 0.9674 | 18.52528 | 34.25319 | 29.14 | 78.44 | Download link |
ResNeXt101_ 32x16d_wsl |
0.8424 | 0.9726 | 25.60395 | 71.88384 | 57.55 | 152.66 | Download link |
ResNeXt101_ 32x32d_wsl |
0.8497 | 0.9759 | 54.87396 | 160.04337 | 115.17 | 303.11 | Download link |
ResNeXt101_ 32x48d_wsl |
0.8537 | 0.9769 | 99.01698256 | 315.91261 | 173.58 | 456.2 | Download link |
Fix_ResNeXt101_ 32x48d_wsl |
0.8626 | 0.9797 | 160.0838242 | 595.99296 | 354.23 | 456.2 | Download link |
EfficientNetB0 | 0.7738 | 0.9331 | 3.442 | 6.11476 | 0.72 | 5.1 | Download link |
EfficientNetB1 | 0.7915 | 0.9441 | 5.3322 | 9.41795 | 1.27 | 7.52 | Download link |
EfficientNetB2 | 0.7985 | 0.9474 | 6.29351 | 10.95702 | 1.85 | 8.81 | Download link |
EfficientNetB3 | 0.8115 | 0.9541 | 7.67749 | 16.53288 | 3.43 | 11.84 | Download link |
EfficientNetB4 | 0.8285 | 0.9623 | 12.15894 | 30.94567 | 8.29 | 18.76 | Download link |
EfficientNetB5 | 0.8362 | 0.9672 | 20.48571 | 61.60252 | 19.51 | 29.61 | Download link |
EfficientNetB6 | 0.8400 | 0.9688 | 32.62402 | - | 36.27 | 42 | Download link |
EfficientNetB7 | 0.8430 | 0.9689 | 53.93823 | - | 72.35 | 64.92 | Download link |
EfficientNetB0_ small |
0.7580 | 0.9258 | 2.3076 | 4.71886 | 0.72 | 4.65 | Download link |
Accuracy and inference time metrics of ResNeSt and RegNet series models are shown as follows. More detailed information can be refered to ResNeSt and RegNet series tutorial.
Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
Flops(G) | Params(M) | Download Address |
---|---|---|---|---|---|---|---|
ResNeSt50_ fast_1s1x64d |
0.8035 | 0.9528 | 3.45405 | 8.72680 | 8.68 | 26.3 | Download link |
ResNeSt50 | 0.8102 | 0.9542 | 6.69042 | 8.01664 | 10.78 | 27.5 | Download link |
RegNetX_4GF | 0.785 | 0.9416 | 6.46478 | 11.19862 | 8 | 22.1 | Download link |
PaddleClas is released under the Apache 2.0 license
Contributions are highly welcomed and we would really appreciate your feedback!!