添加mmclassification-0.24.1代码，删除mmclassification-speed-benchmark

0fd8347d · unknown · cc567e9e · 0fd8347d · 0fd8347d · 0fd8347d
Commit 0fd8347d authored Jan 08, 2023 by unknown
20 changed files
--- a/openmmlab_test/mmclassification-0.24.1/configs/resnet/README.md
+++ b/openmmlab_test/mmclassification-0.24.1/configs/resnet/README.md
+# ResNet
+
+> [Deep Residual Learning for Image Recognition](https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.
+
+The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/26739999/142574068-60cfdeea-c4ec-4c49-abb2-5dc2facafc3b.png" width="40%"/>
+</div>
+
+## Results and models
+
+The pre-trained models on ImageNet-21k are used to fine-tune, and therefore don't have evaluation results.
+
+|     Model      | resolution | Params(M) | Flops(G) |                                                       Download                                                        |
+| :------------: | :--------: | :-------: | :------: | :-------------------------------------------------------------------------------------------------------------------: |
+| ResNet-50-mill |  224x224   |   86.74   |  15.14   | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth) |
+
+*The "mill" means using the mutil-label pretrain weight from [ImageNet-21K Pretraining for the Masses](https://github.com/Alibaba-MIIL/ImageNet21K).*
+
+### Cifar10
+
+|   Model    | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                                    Config                                    |                                    Download                                     |
+| :--------: | :-------: | :------: | :-------: | :-------: | :--------------------------------------------------------------------------: | :-----------------------------------------------------------------------------: |
+| ResNet-18  |   11.17   |   0.56   |   94.82   |   99.87   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.log.json) |
+| ResNet-34  |   21.28   |   1.16   |   95.34   |   99.87   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.log.json) |
+| ResNet-50  |   23.52   |   1.31   |   95.55   |   99.91   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.log.json) |
+| ResNet-101 |   42.51   |   2.52   |   95.58   |   99.87   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.log.json) |
+| ResNet-152 |   58.16   |   3.74   |   95.76   |   99.89   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.log.json) |
+
+### Cifar100
+
+|   Model   | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                                    Config                                     |                                    Download                                     |
+| :-------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------------------: | :-----------------------------------------------------------------------------: |
+| ResNet-50 |   23.71   |   1.31   |   79.90   |   95.19   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb16_cifar100.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar100_20210528-67b58a1b.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar100_20210528-67b58a1b.log.json) |
+
+### ImageNet-1k
+
+|       Model        | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                                  Config                                  |                                  Download                                   |
+| :----------------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------------: | :-------------------------------------------------------------------------: |
+|     ResNet-18      |   11.69   |   1.82   |   69.90   |   89.43   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.log.json) |
+|     ResNet-34      |   21.8    |   3.68   |   73.62   |   91.59   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.log.json) |
+|     ResNet-50      |   25.56   |   4.12   |   76.55   |   93.06   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.log.json) |
+|     ResNet-101     |   44.55   |   7.85   |   77.97   |   94.06   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.log.json) |
+|     ResNet-152     |   60.19   |  11.58   |   78.48   |   94.13   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.log.json) |
+|    ResNetV1C-50    |   25.58   |   4.36   |   77.01   |   93.58   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.log.json) |
+|   ResNetV1C-101    |   44.57   |   8.09   |   78.30   |   94.27   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.log.json) |
+|   ResNetV1C-152    |   60.21   |  11.82   |   78.76   |   94.41   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.log.json) |
+|    ResNetV1D-50    |   25.58   |   4.36   |   77.54   |   93.57   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.log.json) |
+|   ResNetV1D-101    |   44.57   |   8.09   |   78.93   |   94.48   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.log.json) |
+|   ResNetV1D-152    |   60.21   |  11.82   |   79.41   |   94.70   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.log.json) |
+|  ResNet-50 (fp16)  |   25.56   |   4.12   |   76.30   |   93.07   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32-fp16_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.log.json) |
+|  Wide-ResNet-50\*  |   68.88   |  11.44   |   78.48   |   94.08   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/wide-resnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/wide-resnet50_3rdparty_8xb32_in1k_20220304-66678344.pth) |
+| Wide-ResNet-101\*  |  126.89   |  22.81   |   78.84   |   94.28   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth) |
+| ResNet-50 (rsb-a1) |   25.56   |   4.12   |   80.12   |   94.78   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.log.json) |
+| ResNet-50 (rsb-a2) |   25.56   |   4.12   |   79.55   |   94.37   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a2-300e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a2-300e_in1k_20211228-0fd8be6e.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a2-300e_in1k_20211228-0fd8be6e.log.json) |
+| ResNet-50 (rsb-a3) |   25.56   |   4.12   |   78.30   |   93.80   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.log.json) |
+
+*The "rsb" means using the training settings from [ResNet strikes back: An improved training procedure in timm](https://arxiv.org/abs/2110.00476).*
+
+*Models with * are converted from the [official repo](https://github.com/pytorch/vision). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*
+
+### CUB-200-2011
+
+|   Model   |                       Pretrain                       | resolution | Params(M) | Flops(G) | Top-1 (%) |                       Config                       |                       Download                        |
+| :-------: | :--------------------------------------------------: | :--------: | :-------: | :------: | :-------: | :------------------------------------------------: | :---------------------------------------------------: |
+| ResNet-50 | [ImageNet-21k-mill](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth) |  448x448   |   23.92   |  16.48   |   88.45   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb8_cub.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.log.json) |
+
+### Stanford-Cars
+
+|   Model   |                       Pretrain                       | resolution | Params(M) | Flops(G) | Top-1 (%) |                       Config                       |                       Download                        |
+| :-------: | :--------------------------------------------------: | :--------: | :-------: | :------: | :-------: | :------------------------------------------------: | :---------------------------------------------------: |
+| ResNet-50 | [ImageNet-21k-mill](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth) |  448x448   |   23.92   |  16.48   |   92.82   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb8_cars.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cars_20220812-9d85901a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cars_20220812-9d85901a.log.json) |
+
+## Citation
+
+```
+@inproceedings{he2016deep,
+  title={Deep residual learning for image recognition},
+  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={770--778},
+  year={2016}
+}
+```
--- a/openmmlab_test/mmclassification-0.24.1/configs/resnet/metafile.yml
+++ b/openmmlab_test/mmclassification-0.24.1/configs/resnet/metafile.yml
+Collections:
+  - Name: ResNet
+    Metadata:
+      Training Data: ImageNet-1k
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Epochs: 100
+      Batch Size: 256
+      Architecture:
+        - ResNet
+    Paper:
+      URL: https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html
+      Title: "Deep Residual Learning for Image Recognition"
+    README: configs/resnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmclassification/blob/v0.15.0/mmcls/models/backbones/resnet.py#L383
+      Version: v0.15.0
+
+Models:
+  - Name: resnet18_8xb16_cifar10
+    Metadata:
+      Training Data: CIFAR-10
+      Epochs: 200
+      Batch Size: 128
+      FLOPs: 560000000
+      Parameters: 11170000
+    In Collection: ResNet
+    Results:
+      - Dataset: CIFAR-10
+        Metrics:
+          Top 1 Accuracy: 94.82
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth
+    Config: configs/resnet/resnet18_8xb16_cifar10.py
+  - Name: resnet34_8xb16_cifar10
+    Metadata:
+      Training Data: CIFAR-10
+      Epochs: 200
+      Batch Size: 128
+      FLOPs: 1160000000
+      Parameters: 21280000
+    In Collection: ResNet
+    Results:
+      - Dataset: CIFAR-10
+        Metrics:
+          Top 1 Accuracy: 95.34
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.pth
+    Config: configs/resnet/resnet34_8xb16_cifar10.py
+  - Name: resnet50_8xb16_cifar10
+    Metadata:
+      Training Data: CIFAR-10
+      Epochs: 200
+      Batch Size: 128
+      FLOPs: 1310000000
+      Parameters: 23520000
+    In Collection: ResNet
+    Results:
+      - Dataset: CIFAR-10
+        Metrics:
+          Top 1 Accuracy: 95.55
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth
+    Config: configs/resnet/resnet50_8xb16_cifar10.py
+  - Name: resnet101_8xb16_cifar10
+    Metadata:
+      Training Data: CIFAR-10
+      Epochs: 200
+      Batch Size: 128
+      FLOPs: 2520000000
+      Parameters: 42510000
+    In Collection: ResNet
+    Results:
+      - Dataset: CIFAR-10
+        Metrics:
+          Top 1 Accuracy: 95.58
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.pth
+    Config: configs/resnet/resnet101_8xb16_cifar10.py
+  - Name: resnet152_8xb16_cifar10
+    Metadata:
+      Training Data: CIFAR-10
+      Epochs: 200
+      Batch Size: 128
+      FLOPs: 3740000000
+      Parameters: 58160000
+    In Collection: ResNet
+    Results:
+      - Dataset: CIFAR-10
+        Metrics:
+          Top 1 Accuracy: 95.76
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.pth
+    Config: configs/resnet/resnet152_8xb16_cifar10.py
+  - Name: resnet50_8xb16_cifar100
+    Metadata:
+      Training Data: CIFAR-100
+      Epochs: 200
+      Batch Size: 128
+      FLOPs: 1310000000
+      Parameters: 23710000
+    In Collection: ResNet
+    Results:
+      - Dataset: CIFAR-100
+        Metrics:
+          Top 1 Accuracy: 79.90
+          Top 5 Accuracy: 95.19
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar100_20210528-67b58a1b.pth
+    Config: configs/resnet/resnet50_8xb16_cifar100.py
+  - Name: resnet18_8xb32_in1k
+    Metadata:
+      FLOPs: 1820000000
+      Parameters: 11690000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 69.90
+          Top 5 Accuracy: 89.43
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth
+    Config: configs/resnet/resnet18_8xb32_in1k.py
+  - Name: resnet34_8xb32_in1k
+    Metadata:
+      FLOPs: 3680000000
+      Parameters: 2180000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 73.62
+          Top 5 Accuracy: 91.59
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.pth
+    Config: configs/resnet/resnet34_8xb32_in1k.py
+  - Name: resnet50_8xb32_in1k
+    Metadata:
+      FLOPs: 4120000000
+      Parameters: 25560000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 76.55
+          Top 5 Accuracy: 93.06
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth
+    Config: configs/resnet/resnet50_8xb32_in1k.py
+  - Name: resnet101_8xb32_in1k
+    Metadata:
+      FLOPs: 7850000000
+      Parameters: 44550000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 77.97
+          Top 5 Accuracy: 94.06
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth
+    Config: configs/resnet/resnet101_8xb32_in1k.py
+  - Name: resnet152_8xb32_in1k
+    Metadata:
+      FLOPs: 11580000000
+      Parameters: 60190000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.48
+          Top 5 Accuracy: 94.13
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.pth
+    Config: configs/resnet/resnet152_8xb32_in1k.py
+  - Name: resnetv1d50_8xb32_in1k
+    Metadata:
+      FLOPs: 4360000000
+      Parameters: 25580000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 77.54
+          Top 5 Accuracy: 93.57
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth
+    Config: configs/resnet/resnetv1d50_8xb32_in1k.py
+  - Name: resnetv1d101_8xb32_in1k
+    Metadata:
+      FLOPs: 8090000000
+      Parameters: 44570000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.93
+          Top 5 Accuracy: 94.48
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth
+    Config: configs/resnet/resnetv1d101_8xb32_in1k.py
+  - Name: resnetv1d152_8xb32_in1k
+    Metadata:
+      FLOPs: 11820000000
+      Parameters: 60210000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 79.41
+          Top 5 Accuracy: 94.70
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth
+    Config: configs/resnet/resnetv1d152_8xb32_in1k.py
+  - Name: resnet50_8xb32-fp16_in1k
+    Metadata:
+      FLOPs: 4120000000
+      Parameters: 25560000
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+        - Mixed Precision Training
+    In Collection: ResNet
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 76.30
+          Top 5 Accuracy: 93.07
+    Weights: https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth
+    Config: configs/resnet/resnet50_8xb32-fp16_in1k.py
+  - Name: resnet50_8xb256-rsb-a1-600e_in1k
+    Metadata:
+      FLOPs: 4120000000
+      Parameters: 25560000
+      Training Techniques:
+        - LAMB
+        - Weight Decay
+        - Cosine Annealing
+        - Mixup
+        - CutMix
+        - RepeatAugSampler
+        - RandAugment
+      Epochs: 600
+      Batch Size: 2048
+    In Collection: ResNet
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 80.12
+          Top 5 Accuracy: 94.78
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth
+    Config: configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py
+  - Name: resnet50_8xb256-rsb-a2-300e_in1k
+    Metadata:
+      FLOPs: 4120000000
+      Parameters: 25560000
+      Training Techniques:
+        - LAMB
+        - Weight Decay
+        - Cosine Annealing
+        - Mixup
+        - CutMix
+        - RepeatAugSampler
+        - RandAugment
+      Epochs: 300
+      Batch Size: 2048
+    In Collection: ResNet
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 79.55
+          Top 5 Accuracy: 94.37
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a2-300e_in1k_20211228-0fd8be6e.pth
+    Config: configs/resnet/resnet50_8xb256-rsb-a2-300e_in1k.py
+  - Name: resnet50_8xb256-rsb-a3-100e_in1k
+    Metadata:
+      FLOPs: 4120000000
+      Parameters: 25560000
+      Training Techniques:
+        - LAMB
+        - Weight Decay
+        - Cosine Annealing
+        - Mixup
+        - CutMix
+        - RandAugment
+      Batch Size: 2048
+    In Collection: ResNet
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.30
+          Top 5 Accuracy: 93.80
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.pth
+    Config: configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py
+  - Name: resnetv1c50_8xb32_in1k
+    Metadata:
+      FLOPs: 4360000000
+      Parameters: 25580000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 77.01
+          Top 5 Accuracy: 93.58
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.pth
+    Config: configs/resnet/resnetv1c50_8xb32_in1k.py
+  - Name: resnetv1c101_8xb32_in1k
+    Metadata:
+      FLOPs: 8090000000
+      Parameters: 44570000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.30
+          Top 5 Accuracy: 94.27
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.pth
+    Config: configs/resnet/resnetv1c101_8xb32_in1k.py
+  - Name: resnetv1c152_8xb32_in1k
+    Metadata:
+      FLOPs: 11820000000
+      Parameters: 60210000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.76
+          Top 5 Accuracy: 94.41
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.pth
+    Config: configs/resnet/resnetv1c152_8xb32_in1k.py
+  - Name: resnet50_8xb8_cub
+    Metadata:
+      FLOPs: 16480000000
+      Parameters: 23920000
+    In Collection: ResNet
+    Results:
+      - Dataset: CUB-200-2011
+        Metrics:
+          Top 1 Accuracy: 88.45
+        Task: Image Classification
+    Pretrain: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.pth
+    Config: configs/resnet/resnet50_8xb8_cub.py
+  - Name: resnet50_8xb8_cars
+    Metadata:
+      FLOPs: 16480000000
+      Parameters: 23920000
+    In Collection: ResNet
+    Results:
+      - Dataset: StanfordCars
+        Metrics:
+          Top 1 Accuracy: 92.82
+        Task: Image Classification
+    Pretrain: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cars_20220812-9d85901a.pth
+    Config: configs/resnet/resnet50_8xb8_cars.py
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet101_b16x8_cifar10.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet101_b16x8_cifar10.py
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet101_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet101_b32x8_imagenet.py
--- a/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet101_b16x8_cifar10.py
+++ b/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet101_b16x8_cifar10.py
+_base_ = 'resnet101_8xb16_cifar10.py'
+
+_deprecation_ = dict(
+    expected='resnet101_8xb16_cifar10.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)
--- a/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet101_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet101_b32x8_imagenet.py
+_base_ = 'resnet101_8xb32_in1k.py'
+
+_deprecation_ = dict(
+    expected='resnet101_8xb32_in1k.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet152_b16x8_cifar10.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet152_b16x8_cifar10.py
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet152_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet152_b32x8_imagenet.py
--- a/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet152_b16x8_cifar10.py
+++ b/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet152_b16x8_cifar10.py
+_base_ = 'resnet152_8xb16_cifar10.py'
+
+_deprecation_ = dict(
+    expected='resnet152_8xb16_cifar10.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)
--- a/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet152_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet152_b32x8_imagenet.py
+_base_ = 'resnet152_8xb32_in1k.py'
+
+_deprecation_ = dict(
+    expected='resnet152_8xb32_in1k.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet18_b16x8_cifar10.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet18_b16x8_cifar10.py
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet18_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet18_b32x8_imagenet.py
--- a/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet18_b16x8_cifar10.py
+++ b/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet18_b16x8_cifar10.py
+_base_ = 'resnet18_8xb16_cifar10.py'
+
+_deprecation_ = dict(
+    expected='resnet18_8xb16_cifar10.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)
--- a/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet18_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet18_b32x8_imagenet.py
+_base_ = 'resnet18_8xb32_in1k.py'
+
+_deprecation_ = dict(
+    expected='resnet18_8xb32_in1k.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet34_b16x8_cifar10.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet34_b16x8_cifar10.py
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet34_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet34_b32x8_imagenet.py
--- a/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet34_b16x8_cifar10.py
+++ b/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet34_b16x8_cifar10.py
+_base_ = 'resnet34_8xb16_cifar10.py'
+
+_deprecation_ = dict(
+    expected='resnet34_8xb16_cifar10.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)
--- a/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet34_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet34_b32x8_imagenet.py
+_base_ = 'resnet34_8xb32_in1k.py'
+
+_deprecation_ = dict(
+    expected='resnet34_8xb32_in1k.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet50_b64x32_warmup_coslr_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnet50_b64x32_warmup_coslr_imagenet.py
--- a/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet50_32xb64-warmup-lbs_in1k.py
+++ b/openmmlab_test/mmclassification-0.24.1/configs/resnet/resnet50_32xb64-warmup-lbs_in1k.py
+_base_ = ['./resnet50_32xb64-warmup_in1k.py']
+model = dict(
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(
+            type='LabelSmoothLoss',
+            loss_weight=1.0,
+            label_smooth_val=0.1,
+            num_classes=1000),
+    ))