open-mmlab · HIT-cwh · Mar 13, 2023
diff --git a/configs/distill/mmcls/dist/metafile.yml b/configs/distill/mmcls/dist/metafile.yml
@@ -0,0 +1,34 @@
+Collections:
+  - Name: DIST
+    Metadata:
+      Training Data:
+        - ImageNet-1k
+    Paper:
+      URL: https://arxiv.org/abs/2205.10536
+      Title: Knowledge Distillation from A Stronger Teacher
+    README: configs/distill/mmcls/dist/README.md
+
+Models:
+  - Name: dist_logits_resnet34_resnet18_8xb32_in1k
+    In Collection: DIST
+    Metadata:
+      Location: logits
+      Student:
+        Config: mmcls::resnet/resnet18_8xb32_in1k.py
+        Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth
+        Metrics:
+          Top 1 Accuracy: 69.90
+          Top 5 Accuracy: 89.43
+      Teacher:
+        Config: mmcls::resnet/resnet34_8xb32_in1k.py
+        Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.pth
+        Metrics:
+          Top 1 Accuracy: 73.62
+          Top 5 Accuracy: 91.59
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 71.61
+    Config: configs/distill/mmcls/dist/dist_logits_resnet34_resnet18_8xb32_in1k.py
+    Weights: https://download.openmmlab.com/mmrazor/v1/distillation/dist_logits_resnet34_resnet18_8xb32_in1k.pth
diff --git a/configs/distill/mmdet/mgd/metafile.yml b/configs/distill/mmdet/mgd/metafile.yml
@@ -0,0 +1,32 @@
+Collections:
+  - Name: MGD
+    Metadata:
+      Training Data:
+        - COCO
+    Paper:
+      URL: https://arxiv.org/abs/2205.01529
+      Title: Masked Generative Distillation
+    README: configs/distill/mmdet/mgd/README.md
+
+Models:
+  - Name: mgd_fpn_retina_x101_retina_r50_2x_coco
+    In Collection: MGD
+    Metadata:
+      Location: FPN
+      Student:
+        Metrics:
+          box AP: 37.4
+        Config: mmdet::retinanet/retinanet_r50_fpn_2x_coco.py
+        Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth
+      Teacher:
+        Metrics:
+          box AP: 41.0
+        Config: mmdet::retinanet/retinanet_x101-64x4d_fpn_1x_coco.py
+        Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.0
+    Config: configs/distill/mmdet/mgd/mgd_fpn_retina_x101_retina_r50_2x_coco.py
+    Weights: https://download.openmmlab.com/mmrazor/v1/mgd/mgd_fpn_retina_x101_retina_r50_2x_coco_20221209_191847-87141529.pth
diff --git a/configs/nas/mmcls/autoformer/metafile.yml b/configs/nas/mmcls/autoformer/metafile.yml
@@ -0,0 +1,27 @@
+Collections:
+  - Name: AUTOFORMER
+    Metadata:
+      Training Data:
+        - ImageNet-1k
+    Paper:
+      URL: https://arxiv.org/abs/2107.00651
+      Title: Searching Transformers for Visual Recognition
+    README: configs/nas/mmcls/autoformer/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmrazor/blob/dev-1.x/mmrazor/models/algorithms/nas/autoformer.py
+      Version: V1.0.0rc2
+
+Models:
+  - Name: autoformer_subnet_8xb256_in1k
+    In Collection: AUTOFORMER
+    Metadata:
+      Flops(G): 10.57
+      Params(M): 54.319
+      Subnet: https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/nas/mmcls/autoformer/AUTOFORMER_SUBNET_B.yaml
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 82.47
+    Config: configs/nas/mmcls/autoformer/autoformer_subnet_8xb256_in1k.py
+    Weights: https://download.openmmlab.com/mmrazor/v1/autoformer/autoformer_supernet_32xb256_in1k_20220919_110144-c658ce8f.pth
diff --git a/configs/nas/mmcls/bignas/README.md b/configs/nas/mmcls/bignas/README.md
@@ -37,11 +37,11 @@ CUDA_VISIBLE_DEVICES=0 PORT=29500 ./tools/dist_test.sh \
 
 ## Results and models
 
-| Dataset  |       Supernet       |                                                              Subnet                                                               |       Params(M)        |       Flops(G)       |          Top-1          |                                                              Config                                                               |                                                                       Download                                                                       |                                                                      Remarks                                                                      |
-| :------: | :------------------: | :-------------------------------------------------------------------------------------------------------------------------------: | :--------------------: | :------------------: | :---------------------: | :-------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------: |
-| ImageNet | AttentiveMobileNetV3 | [search space](https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/_base_/nas_backbones/attentive_mobilenetv3_supernet.py) | 8.854(min) / 23.3(max) | 212(min) / 1944(max) | 77.19(min) / 81.42(max) | [config](https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/nas/mmcls/bignas/attentive_mobilenet_supernet_32xb64_in1k.py) | [model\*](https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_supernet_32xb64_in1k_flops-2G_acc-81.72_20221229_200440-954772a3.pth) | [log](https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_supernet_32xb64_in1k_20221227_175800-bcf94eaa.json)  (`sandwich rule`) |
-| ImageNet | AttentiveMobileNetV3 |                  [AttentiveNAS-A0\*](https://download.openmmlab.com/mmrazor/v1/bignas/ATTENTIVE_SUBNET_A0.yaml)                   |         8.854          |         212          |          77.19          |  [config](https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/nas/mmcls/bignas/attentive_mobilenet_subnet_8xb256_in1k.py)  | [model](https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_subnet_8xb256_in1k_flops-0.21G_acc-77.19_20221229_200440-282a1f70.pth)  |                                                              Converted from the repo                                                              |
-| ImageNet | AttentiveMobileNetV3 |                  [AttentiveNAS-A6\*](https://download.openmmlab.com/mmrazor/v1/bignas/ATTENTIVE_SUBNET_A6.yaml)                   |         15.594         |         927          |          80.81          |  [config](https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/nas/mmcls/bignas/attentive_mobilenet_subnet_8xb256_in1k.py)  | [model](https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_subnet_8xb256_in1k_flops-0.93G_acc-80.81_20221229_200440-73d92cc6.pth)  |                                                              Converted from the repo                                                              |
+| Dataset  |       Supernet       |                                                              Subnet                                                               |       Params(M)        |         Flops(G)         |          Top-1          |                                                              Config                                                               |                                                                       Download                                                                       |                                                                      Remarks                                                                      |
+| :------: | :------------------: | :-------------------------------------------------------------------------------------------------------------------------------: | :--------------------: | :----------------------: | :---------------------: | :-------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------: |
+| ImageNet | AttentiveMobileNetV3 | [search space](https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/_base_/nas_backbones/attentive_mobilenetv3_supernet.py) | 8.854(min) / 23.3(max) | 0.212(min) / 0.1944(max) | 77.19(min) / 81.42(max) | [config](https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/nas/mmcls/bignas/attentive_mobilenet_supernet_32xb64_in1k.py) | [model\*](https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_supernet_32xb64_in1k_flops-2G_acc-81.72_20221229_200440-954772a3.pth) | [log](https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_supernet_32xb64_in1k_20221227_175800-bcf94eaa.json)  (`sandwich rule`) |
+| ImageNet | AttentiveMobileNetV3 |                  [AttentiveNAS-A0\*](https://download.openmmlab.com/mmrazor/v1/bignas/ATTENTIVE_SUBNET_A0.yaml)                   |         8.854          |          0.212           |          77.19          |  [config](https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/nas/mmcls/bignas/attentive_mobilenet_subnet_8xb256_in1k.py)  | [model](https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_subnet_8xb256_in1k_flops-0.21G_acc-77.19_20221229_200440-282a1f70.pth)  |                                                              Converted from the repo                                                              |
+| ImageNet | AttentiveMobileNetV3 |                  [AttentiveNAS-A6\*](https://download.openmmlab.com/mmrazor/v1/bignas/ATTENTIVE_SUBNET_A6.yaml)                   |         15.594         |          0.927           |          80.81          |  [config](https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/nas/mmcls/bignas/attentive_mobilenet_subnet_8xb256_in1k.py)  | [model](https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_subnet_8xb256_in1k_flops-0.93G_acc-80.81_20221229_200440-73d92cc6.pth)  |                                                              Converted from the repo                                                              |
 
 *Models with * are converted from the [official repo](https://github.com/facebookresearch/AttentiveNAS). The config files of these models
 are only for inference. We support training the supernet by `sandwich rule`, which is different from `rejection sampling` in [official repo](https://github.com/facebookresearch/AttentiveNAS), and welcome you to contribute your reproduction results.*

diff --git a/configs/nas/mmcls/bignas/metafile.yml b/configs/nas/mmcls/bignas/metafile.yml
@@ -0,0 +1,43 @@
+Collections:
+  - Name: BigNAS
+    Metadata:
+      Training Data:
+        - ImageNet-1k
+    Paper:
+      URL: https://arxiv.org/abs/2003.11142
+      Title: BigNAS Scaling Up Neural Architecture Search with Big Single-Stage Models
+    README: configs/nas/mmcls/bignas/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmrazor/blob/dev-1.x/mmrazor/models/algorithms/nas/bignas.py
+      Version: V1.0.0rc2
+
+Models:
+  - Name: attentive_mobilenet_subnet_8xb256_in1k_flops-927M
+    In Collection: BigNAS
+    Metadata:
+      Flops(G): 0.927
+      Params(M): 15.594
+      Supernet: AttentiveMobileNetV3
+      Subnet: https://download.openmmlab.com/mmrazor/v1/bignas/ATTENTIVE_SUBNET_A6.yaml
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 80.81
+    Config: configs/nas/mmcls/bignas/attentive_mobilenet_subnet_8xb256_in1k.py
+    Weights: https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_subnet_8xb256_in1k_flops-0.93G_acc-80.81_20221229_200440-73d92cc6.pth
+
+  - Name: attentive_mobilenet_subnet_8xb256_in1k_flops-212M
+    In Collection: BigNAS
+    Metadata:
+      Flops(G): 0.212
+      Params(M): 8.854
+      Supernet: AttentiveMobileNetV3
+      Channel: https://download.openmmlab.com/mmrazor/v1/bignas/ATTENTIVE_SUBNET_A0.yaml
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 77.19
+    Config: configs/nas/mmcls/bignas/attentive_mobilenet_subnet_8xb256_in1k.py
+    Weights: https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_subnet_8xb256_in1k_flops-0.21G_acc-77.19_20221229_200440-282a1f70.pth
diff --git a/configs/nas/mmcls/dsnas/README.md b/configs/nas/mmcls/dsnas/README.md
@@ -34,9 +34,9 @@ CUDA_VISIBLE_DEVICES=0 PORT=29500 ./tools/dist_test.sh \
 
 ### Supernet
 
-| Dataset  | Params(M) | FLOPs (G) | Top-1 Acc (%) | Top-5 Acc (%) |                  Config                   |                                                                                                                         Download                                                                                                                         |     Remarks      |
-| :------: | :-------: | :-------: | :-----------: | :-----------: | :---------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------: |
-| ImageNet |   3.33    |   0.299   |     73.56     |     91.24     | [config](./dsnas_supernet_8xb128_in1k.py) | [model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v1/dsnas/dsnas_supernet_8xb128_in1k_20220926_171954-29b87e3a.pth) \| [log](https://download.openmmlab.com/mmrazor/v1/dsnas/dsnas_supernet_8xb128_in1k_20220926_171954-29b87e3a.log) | MMRazor searched |
+| Dataset  | Params(M) | FLOPs (G) | Top-1 Acc (%) | Top-5 Acc (%) |                  Config                   |                                                                                                              Download                                                                                                              |     Remarks      |
+| :------: | :-------: | :-------: | :-----------: | :-----------: | :---------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------: |
+| ImageNet |   3.33    |   0.299   |     73.56     |     91.24     | [config](./dsnas_supernet_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmrazor/v1/dsnas/dsnas_supernet_8xb128_in1k_20220926_171954-29b87e3a.pth) \| [log](https://download.openmmlab.com/mmrazor/v1/dsnas/dsnas_supernet_8xb128_in1k_20220926_171954-29b87e3a.log) | MMRazor searched |
 
 **Note**:
 

diff --git a/configs/nas/mmcls/dsnas/metafile.yml b/configs/nas/mmcls/dsnas/metafile.yml
@@ -0,0 +1,27 @@
+Collections:
+  - Name: DSNAS
+    Metadata:
+      Training Data:
+        - ImageNet-1k
+    Paper:
+      URL: https://arxiv.org/abs/2002.09128.pdf
+      Title: DSNAS Direct Neural Architecture Search without Parameter Retraining
+    README: configs/nas/mmcls/dsnas/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmrazor/blob/dev-1.x/mmrazor/models/algorithms/nas/dsnas.py
+      Version: V1.0.0rc2
+
+Models:
+  - Name: dsnas_subnet_8xb128_in1k
+    In Collection: DSNAS
+    Metadata:
+      Flops(G): 0.299
+      Params(M): 3.33
+      Subnet: https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/nas/mmcls/dsnas/DSNAS_SUBNET_IMAGENET_PAPER_ALIAS.yaml
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 73.56
+    Config: configs/nas/mmcls/dsnas/dsnas_subnet_8xb128_in1k.py
+    Weights: https://download.openmmlab.com/mmrazor/v1/dsnas/dsnas_supernet_8xb128_in1k_20220926_171954-29b87e3a.pth
diff --git a/configs/nas/mmcls/onceforall/README.md b/configs/nas/mmcls/onceforall/README.md
@@ -25,9 +25,9 @@ CUDA_VISIBLE_DEVICES=0 PORT=29500 ./tools/dist_test.sh \
 
 | Dataset  |       Supernet       |                                                           Subnet                                                            | Params(M) | Flops(G) | Top-1 |                                                             Config                                                              |                                                                             Download                                                                             |         Remarks         |
 | :------: | :------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :-------: | :------: | :---: | :-----------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------: |
-| ImageNet | AttentiveMobileNetV3 | [search space](https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/_base_/nas_backbones/ofa_mobilenetv3_supernet.py) |    7.6    |  747.8   | 77.5  | [config](https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/nas/mmcls/onceforall/ofa_mobilenet_supernet_32xb64_in1k.py) |                 [model](https://download.openmmlab.com/mmrazor/v1/ofa/ofa_mobilenet_supernet_d234_e346_k357_w1_0.py_20221214_0940-d0ebc66f.pth)                  | Converted from the repo |
-| ImageNet | AttentiveMobileNetV3 |                                            note8_lat@22ms_top1@70.4_finetune@25                                             |    4.3    |   70.9   | 70.3  |                   [config](https://download.openmmlab.com/mmrazor/v1/ofa/rtmdet/OFA_SUBNET_NOTE8_LAT22.yaml)                    | [model](https://download.openmmlab.com/mmrazor/v1/ofa/ofa_mobilenet_subnet_8xb256_in1k_note8_lat%4022ms_top1%4070.4_finetune%4025.py_20221214_0938-fb7fb84f.pth) | Converted from the repo |
-| ImageNet | AttentiveMobileNetV3 |                                            note8_lat@31ms_top1@72.8_finetune@25                                             |    4.6    |  105.4   | 72.6  |                   [config](https://download.openmmlab.com/mmrazor/v1/ofa/rtmdet/OFA_SUBNET_NOTE8_LAT31.yaml)                    | [model](https://download.openmmlab.com/mmrazor/v1/ofa/ofa_mobilenet_subnet_8xb256_in1k_note8_lat%4031ms_top1%4072.8_finetune%4025.py_20221214_0939-981a8b2a.pth) | Converted from the repo |
+| ImageNet | AttentiveMobileNetV3 | [search space](https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/_base_/nas_backbones/ofa_mobilenetv3_supernet.py) |    7.6    |  0.747   | 77.5  | [config](https://github.com/open-mmlab/mmrazor/blob/dev-1.x/configs/nas/mmcls/onceforall/ofa_mobilenet_supernet_32xb64_in1k.py) |                 [model](https://download.openmmlab.com/mmrazor/v1/ofa/ofa_mobilenet_supernet_d234_e346_k357_w1_0.py_20221214_0940-d0ebc66f.pth)                  | Converted from the repo |
+| ImageNet | AttentiveMobileNetV3 |                                            note8_lat@22ms_top1@70.4_finetune@25                                             |    4.3    |  0.070   | 70.3  |                   [config](https://download.openmmlab.com/mmrazor/v1/ofa/rtmdet/OFA_SUBNET_NOTE8_LAT22.yaml)                    | [model](https://download.openmmlab.com/mmrazor/v1/ofa/ofa_mobilenet_subnet_8xb256_in1k_note8_lat%4022ms_top1%4070.4_finetune%4025.py_20221214_0938-fb7fb84f.pth) | Converted from the repo |
+| ImageNet | AttentiveMobileNetV3 |                                            note8_lat@31ms_top1@72.8_finetune@25                                             |    4.6    |  0.105   | 72.6  |                   [config](https://download.openmmlab.com/mmrazor/v1/ofa/rtmdet/OFA_SUBNET_NOTE8_LAT31.yaml)                    | [model](https://download.openmmlab.com/mmrazor/v1/ofa/ofa_mobilenet_subnet_8xb256_in1k_note8_lat%4031ms_top1%4072.8_finetune%4025.py_20221214_0939-981a8b2a.pth) | Converted from the repo |
 
 **Note**: