[Docs] Add Algorithm Metafiles

configs/distill/cwd/metafile.yml

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+Collections:
+  - Name: CWD
+    Metadata:
+      Training Data:
+        - Cityscapes
+        - COCO
+    Paper:
+      URL: https://arxiv.org/abs/2011.13256
+      Title: Channel-wise Knowledge Distillation for Dense Prediction
+    README: configs/distill/cwd/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmrazor/blob/v0.1.0/mmrazor/models/losses/cwd.py#L10
+      Version: v0.1.0
+    Converted From:
+      Code:
+        - https://github.com/pppppM/mmsegmentation-distiller
+        - https://github.com/pppppM/mmdetection-distiller
+Models:
+  - Name: cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k
+    In Collection: CWD
+    Metadata:
+      Location: cls head
+      Student: pspnet-r18-d8
+      Teacher: pspnet-r101-d8
+      Teacher Checkpoint: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth
+    Results:
+      - Task: Semantic Segmentation
+        Dataset: Cityscapes
+        Metrics:
+          mIoU: 75.54
+          mIoU(S): 74.87
+          mIoU(T): 79.76
+    Config: configs/distill/cwd/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k.py
+    Weights: https://download.openmmlab.com/mmrazor/v0.1/distill/cwd/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k_mIoU-75.54_20211222-3a26ee1c.pth
+  - Name: cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco
+    In Collection: CWD
+    Metadata:
+      Location: cls head
+      Student: gfl-r50-fpn
+      Teacher: gfl-r101-fpn
+      Teacher Checkpoint: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.9
+          box AP(S): 40.2
+          box AP(T): 44.7
+    Config: configs/distill/cwd/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco.py
+    Weights: https://download.openmmlab.com/mmrazor/v0.1/distill/cwd/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco_20211222-655dff39.pth

configs/distill/wsld/metafile.yml

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+Collections:
+  - Name: WSLD
+    Metadata:
+      Training Data:
+        - ImageNet-1k
+    Paper:
+      URL: https://arxiv.org/abs/2102.00650
+      Title: Rethinking Soft Labels for Knowledge Distillation:A Bias-Variance Tradeoff Perspective
+    README: configs/distill/wsld/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmrazor/blob/v0.1.0/mmrazor/models/losses/weighted_soft_label_distillation.py
+      Version: v0.1.0
+    Converted From:
+      Code: https://github.com/bellymonster/Weighted-Soft-Label-Distillation
+Models:
+  - Name: wsld_cls_head_resnet34_resnet18_8xb32_in1k
+    In Collection: WSLD
+    Metadata:
+      Location: cls head
+      Student: R-18
+      Teacher: R-34
+      Teacher Checkpoint: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 71.54
+          Top 1 Accuracy:(S): 69.90
+          Top 1 Accuracy:(T): 73.62
+    Config: configs/distill/wsld/wsld_cls_head_resnet34_resnet18_8xb32_in1k.py
+    Weights: https://download.openmmlab.com/mmrazor/v0.1/distill/wsld/wsld_cls_head_resnet34_resnet18_8xb32_in1k/wsld_cls_head_resnet34_resnet18_8xb32_in1k_acc-71.54_20211222-91f28cf6.pth

configs/nas/darts/metafile.yml

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+Collections:
+  - Name: Darts
+    Metadata:
+      Training Data:
+        - CIFAR-10
+    Paper:
+      URL: https://arxiv.org/abs/1806.09055
+      Title: DARTS:Differentiable Architecture Search
+    README: configs/nas/darts/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmrazor/blob/v0.1.0/mmrazor/models/algorithms/darts.py
+      Version: v0.1.0
+    Converted From:
+      Code: https://github.com/quark0/darts
+Models:
+  - Name: darts_subnetnet_1xb96_cifar10
+    In Collection: Darts
+    Metadata:
+      Params(M): 3.42
+      Mutable: https://download.openmmlab.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_acc-97.32_20211222-e5727921_mutable_cfg.yaml
+    Results:
+      - Task: Image Classification
+        Dataset: CIFAR-10
+        Metrics:
+          Top 1 Accuracy: 97.32
+          Top 5 Accuracy: 99.94
+    Config: configs/nas/darts/darts_subnetnet_1xb96_cifar10.py
+    Weights: https://download.openmmlab.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_acc-97.32_20211222-e5727921.pth

configs/nas/detnas/metafile.yml

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+Collections:
+  - Name: DetNAS
+    Metadata:
+      Training Data:
+        - ImageNet-1k
+        - COCO
+    Paper:
+      URL: https://arxiv.org/abs/1903.10979
+      Title: DetNAS:Backbone Search for Object Detection
+    README: configs/nas/detnas/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmrazor/blob/v0.1.0/mmrazor/models/algorithms/detnas.py
+      Version: v0.1.0
+    Converted From:
+      Code: https://github.com/megvii-model/DetNAS
+Models:
+  - Name: detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco
+    In Collection: DetNAS
+    Metadata:
+      FLOPs(Backbone): 340 MB
+      Params(Backbone): 3.35 MB
+      Supernet: FRCNN-ShuffleNetV2
+      Mutable: https://download.openmmlab.com/mmrazor/v0.1/nas/spos/spos_shufflenetv2_subnet_8xb128_in1k/spos_shufflenetv2_subnet_8xb128_in1k_flops_0.33M_acc_73.87_20211222-454627be_mutable_cfg.yaml
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.5
+    Config: configs/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco.py
+    Weights: https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f_mutable_cfg.yaml

configs/nas/spos/metafile.yml

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+Collections:
+  - Name: SPOS
+    Metadata:
+      Training Data:
+        - ImageNet-1k
+    Paper:
+      URL: https://arxiv.org/abs/1904.00420
+      Title: Single Path One-Shot Neural Architecture Search with Uniform Sampling
+    README: configs/nas/spos/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmrazor/blob/v0.1.0/mmrazor/models/algorithms/spos.py
+      Version: v0.1.0
+    Converted From:
+      Code: https://github.com/megvii-model/SinglePathOneShot
+Models:
+  - Name: spos_subnet_shufflenetv2_8xb128_in1k
+    In Collection: SPOS
+    Metadata:
+      FLOPs: 330 MB
+      Supernet: ShuffleNetV2
+      Mutable: https://download.openmmlab.com/mmrazor/v0.1/nas/spos/spos_shufflenetv2_subnet_8xb128_in1k/spos_shufflenetv2_subnet_8xb128_in1k_flops_0.33M_acc_73.87_20211222-454627be_mutable_cfg.yaml
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 73.87
+          Top 5 Accuracy: 91.60
+    Config: configs/nas/spos/spos_subnet_shufflenetv2_8xb128_in1k.py
+    Weights: https://download.openmmlab.com/mmrazor/v0.1/nas/spos/spos_shufflenetv2_subnet_8xb128_in1k/spos_shufflenetv2_subnet_8xb128_in1k_flops_0.33M_acc_73.87_20211222-1f0a0b4d.pth

configs/pruning/autoslim/metafile.yml

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+Collections:
+  - Name: AutoSlim
+    Metadata:
+      Training Data:
+        - ImageNet-1k
+    Paper:
+      URL: https://arxiv.org/abs/1903.11728
+      Title: AutoSlim:Towards One-Shot Architecture Search for Channel Numbers
+    README: configs/nas/spos/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmrazor/blob/v0.1.0/mmrazor/models/algorithms/autoslim.py
+      Version: v0.1.0
+    Converted From:
+      Code: https://github.com/JiahuiYu/slimmable_networks
+Models:
+  - Name: autoslim_mbv2_subnet_8xb256_in1k
+    In Collection: AutoSlim
+    Metadata:
+      Flops(G): 0.53
+      Params(M): 6.5
+      Supernet: MobileNet v2(x1.5)
+      Channel: https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.53M_acc-74.23_20211222-e5208bbd_channel_cfg.yaml
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 74.23
+          Top 5 Accuracy: 91.74
+    Config: configs/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k.py
+    Weights: https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.53M_acc-74.23_20211222-e5208bbd.pth
+  - Name: autoslim_mbv2_subnet_8xb256_in1k
+    In Collection: AutoSlim
+    Metadata:
+      Flops(G): 0.32
+      Params(M): 5.77
+      Supernet: MobileNet v2(x1.5)
+      Channel: https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.32M_acc-72.73_20211222-b5b0b33c_channel_cfg.yaml
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 72.73
+          Top 5 Accuracy: 90.83
+    Config: configs/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k.py
+    Weights: https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.32M_acc-72.73_20211222-b5b0b33c.pth
+  - Name: autoslim_mbv2_subnet_8xb256_in1k
+    In Collection: AutoSlim
+    Metadata:
+      Flops(G): 0.22
+      Params(M): 4.13
+      Supernet: MobileNet v2(x1.5)
+      Channel: https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.22M_acc-71.39_20211222-43117c7b_channel_cfg.yaml.?
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 74.23
+          Top 5 Accuracy: 91.74
+    Config: configs/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k.py
+    Weights: https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.22M_acc-71.39_20211222-43117c7b.pth

model-index.yml

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+Import:
+  - configs/distill/cwd/metafile.yml
+  - configs/distill/wsld/metafile.yml
+  - configs/nas/darts/metafile.yml
+  - configs/nas/detnas/metafile.yml
+  - configs/nas/spos/metafile.yml
+  - configs/pruning/autoslim/metafile.yml