[Benchmark] rename linear probing config file names

configs/benchmarks/classification/imagenet/resnet50-sobel_8xb32-steplr-100e_in1k.py → configs/benchmarks/classification/imagenet/resnet50-sobel_linear-8xb32-steplr-100e_in1k.py

@@ -1,4 +1,4 @@
-_base_ = 'resnet50_8xb32-steplr-100e_in1k.py'
+_base_ = 'resnet50_linear-8xb32-steplr-100e_in1k.py'
 
 # model settings
 model = dict(with_sobel=True, backbone=dict(in_channels=2, frozen_stages=4))

configs/benchmarks/classification/imagenet/resnet50-sobel_mhead_8xb32-steplr-90e_in1k.py → configs/benchmarks/classification/imagenet/resnet50-sobel_mhead_linear-8xb32-steplr-90e_in1k.py

@@ -1,4 +1,4 @@
-_base_ = 'resnet50_mhead_8xb32-steplr-90e_in1k.py'
+_base_ = 'resnet50_mhead_linear-8xb32-steplr-90e_in1k.py'
 
 # model settings
 model = dict(with_sobel=True, backbone=dict(in_channels=2, frozen_stages=4))

configs/selfsup/byol/README.md

@@ -34,9 +34,9 @@ Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
 
 #### ImageNet Linear Evaluation
 
-The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_8xb32-steplr-90e.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_8xb32-steplr-90e_in1k.py) for details of config.
+The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_linear-8xb32-steplr-90e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_linear-8xb32-steplr-90e_in1k.py) for details of config.
 
-The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_8xb32-steplr-100e_in1k.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k.py) for details of config.
+The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_linear-8xb32-steplr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py) for details of config.
 
 | Self-Supervised Config                                                                                                                                       | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
 | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------- | -------- | -------- | -------- | -------- | ------- |

configs/selfsup/deepcluster/README.md

@@ -34,9 +34,9 @@ Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
 
 #### ImageNet Linear Evaluation
 
-The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_8xb32-steplr-90e.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_8xb32-steplr-90e_in1k.py) for details of config.
+The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_linear-8xb32-steplr-90e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_linear-8xb32-steplr-90e_in1k.py) for details of config.
 
-The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_8xb32-steplr-100e_in1k.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k.py) for details of config.
+The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_linear-8xb32-steplr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py) for details of config.
 
 | Self-Supervised Config                                                                                                                                                   | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
 | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------- | -------- | -------- | -------- | -------- | ------- |

configs/selfsup/densecl/README.md

@@ -34,9 +34,9 @@ Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
 
 #### ImageNet Linear Evaluation
 
-The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_8xb32-steplr-90e.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_8xb32-steplr-90e_in1k.py) for details of config.
+The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_linear-8xb32-steplr-90e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_linear-8xb32-steplr-90e_in1k.py) for details of config.
 
-The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_8xb32-steplr-100e_in1k.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k.py) for details of config.
+The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_linear-8xb32-steplr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py) for details of config.
 
 | Self-Supervised Config                                                                                                                             | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
 | -------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- | ------- |

configs/selfsup/mae/README.md

@@ -35,9 +35,9 @@ for 400 epochs, the details are below:
 
 
 
-| Backbone | Pre-train epoch | Fine-tuning Top-1 |                  Pre-train Config                   |                                    Fine-tuning Config                                     |                                                                                                                        Download                                                                                                                         |
-| :------: | :-------------: | :---------------: | :-------------------------------------------------: | :---------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| ViT-B/16 |       400       |       83.1        | [config](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/mae/mae_vit-b-p16_8xb512-coslr-400e_in1k.py) | [config](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/vit-b-p16_ft-8xb128-coslr-100e_in1k.py) | [model](https://download.openmmlab.com/mmselfsup/mae/mae_vit-base-p16_8xb512-coslr-400e_in1k-224_20220223-85be947b.pth) | [log](https://download.openmmlab.com/mmselfsup/mae/mae_vit-base-p16_8xb512-coslr-300e_in1k-224_20220210_140925.log.json) |
+| Backbone | Pre-train epoch | Fine-tuning Top-1 |                                                     Pre-train Config                                                      |                                                                 Fine-tuning Config                                                                 |                                                                                                                        Download                                                                                                                         |
+| :------: | :-------------: | :---------------: | :-----------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| ViT-B/16 |       400       |       83.1        | [config](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/mae/mae_vit-b-p16_8xb512-coslr-400e_in1k.py) | [config](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb128-coslr-100e_in1k.py) | [model](https://download.openmmlab.com/mmselfsup/mae/mae_vit-base-p16_8xb512-coslr-400e_in1k-224_20220223-85be947b.pth) | [log](https://download.openmmlab.com/mmselfsup/mae/mae_vit-base-p16_8xb512-coslr-300e_in1k-224_20220210_140925.log.json) |
 
 
 ## Citation

configs/selfsup/mocov2/README.md

@@ -34,9 +34,9 @@ Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
 
 #### ImageNet Linear Evaluation
 
-The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_8xb32-steplr-90e.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_8xb32-steplr-90e_in1k.py) for details of config.
+The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_linear-8xb32-steplr-90e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_linear-8xb32-steplr-90e_in1k.py) for details of config.
 
-The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_8xb32-steplr-100e_in1k.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k.py) for details of config.
+The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_linear-8xb32-steplr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py) for details of config.
 
 | Self-Supervised Config                                                                                                                           | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
 | ------------------------------------------------------------------------------------------------------------------------------------------------ | -------- | -------- | -------- | -------- | -------- | ------- |

configs/selfsup/mocov3/README.md

@@ -26,9 +26,9 @@ The classification benchmarks includes 4 downstream task datasets, **VOC**, **Im
 
 The **Linear Evaluation** result is obtained by training a linear head upon the pre-trained backbone. Please refer to [vit-small-p16_8xb128-coslr-90e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/vit-small-p16_8xb128-coslr-90e_in1k.py) for details of config.
 
-| Self-Supervised Config                                                                                                                                                                | Linear Evaluation |
-| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------- |
-| [vit-small-p16_32xb128-fp16-coslr-300e_in1k-224](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/mocov3/mocov3_vit-small-p16_32xb128-fp16-coslr-300e_in1k-224.py) | 73.19             |
+| Self-Supervised Config                                                                                                                                                                              | Linear Evaluation |
+| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------- |
+| [vit-small-p16_linear-32xb128-fp16-coslr-300e_in1k-224](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/mocov3/mocov3_vit-small-p16_linear-32xb128-fp16-coslr-300e_in1k-224.py) | 73.19             |
 
 ## Citation
 

configs/selfsup/npid/README.md

@@ -38,9 +38,9 @@ Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
 
 #### ImageNet Linear Evaluation
 
-The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_8xb32-steplr-90e.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_8xb32-steplr-90e_in1k.py) for details of config.
+The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_linear-8xb32-steplr-90e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_linear-8xb32-steplr-90e_in1k.py) for details of config.
 
-The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_8xb32-steplr-100e_in1k.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k.py) for details of config.
+The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_linear-8xb32-steplr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py) for details of config.
 
 | Self-Supervised Config                                                                                                                         | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
 | ---------------------------------------------------------------------------------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- | ------- |

configs/selfsup/odc/README.md

@@ -34,9 +34,9 @@ Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
 
 #### ImageNet Linear Evaluation
 
-The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_8xb32-steplr-90e.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_8xb32-steplr-90e_in1k.py) for details of config.
+The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_linear-8xb32-steplr-90e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_linear-8xb32-steplr-90e_in1k.py) for details of config.
 
-The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_8xb32-steplr-100e_in1k.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k.py) for details of config.
+The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_linear-8xb32-steplr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py) for details of config.
 
 | Self-Supervised Config                                                                                                                       | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
 | -------------------------------------------------------------------------------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- | ------- |

configs/selfsup/relative_loc/README.md

@@ -34,9 +34,9 @@ Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
 
 #### ImageNet Linear Evaluation
 
-The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_8xb32-steplr-90e.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_8xb32-steplr-90e_in1k.py) for details of config.
+The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_linear-8xb32-steplr-90e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_linear-8xb32-steplr-90e_in1k.py) for details of config.
 
-The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_8xb32-steplr-100e_in1k.py](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k.py) for details of config.
+The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [resnet50_linear-8xb32-steplr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py) for details of config.
 
 | Self-Supervised Config                                                                                                                                       | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
 | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------- | -------- | -------- | -------- | -------- | ------- |