[Fix]Fix metafile (#335)

* add mytrain.py for test * test before layers * test attr in layers * test classifier * delete mytrain.py * add mobilenet_v3.yml * fix ymal * rename model_zoo.yml to model-index.yml * replace imagenet with imagenet-1k
2021-07-01 20:50:42 +08:00 · 2021-07-01 20:50:42 +08:00 · 5a86288f1f
parent 076ee10cac
commit 5a86288f1f
10 changed files with 490 additions and 448 deletions
--- a/configs/fp16/metafile.yml
+++ b/configs/fp16/metafile.yml
@ -1,30 +1,30 @@
 Collections:
  - Name: FP16
    Metadata:
-      Training Data: ImageNet
-      Training Resources: 8x V100 GPUs
+      Training Data: ImageNet-1k
      Training Techniques:
        - SGD with Momentum
        - Weight Decay
        - Mixed Precision Training
+      Training Resources: 8x V100 GPUs
    Paper: https://arxiv.org/abs/1710.03740
    README: configs/fp16/README.md

 Models:
- Config: configs/fp16/resnet50_b32x8_fp16_dynamic_imagenet.py
-  In Collection: FP16
-  Metadata:
-    FLOPs: 4120000000
-    Parameters: 25560000
-    Epochs: 100
-    Batch Size: 256
-    Architecture:
-      - ResNet
-  Name: resnet50_b32x8_fp16_dynamic_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 76.32
-      Top 5 Accuracy: 93.04
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth
+  - Name: resnet50_b32x8_fp16_dynamic_imagenet
+    Metadata:
+      FLOPs: 4120000000
+      Parameters: 25560000
+      Epochs: 100
+      Batch Size: 256
+      Architecture:
+        - ResNet
+    In Collection: FP16
+    Results:
+      - Task: Image Classification
+        Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 76.32
+          Top 5 Accuracy: 93.04
+    Weights: https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth
+    Config: configs/fp16/resnet50_b32x8_fp16_dynamic_imagenet.py
--- a/configs/mobilenet_v2/metafile.yml
+++ b/configs/mobilenet_v2/metafile.yml
@ -1,7 +1,7 @@
 Collections:
  - Name: MobileNet V2
    Metadata:
-      Training Data: ImageNet
+      Training Data: ImageNet-1k
      Training Techniques:
        - SGD with Momentum
        - Weight Decay
@ -14,16 +14,16 @@ Collections:
    README: configs/mobilenet_v2/README.md

 Models:
- Config: configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py
-  In Collection: MobileNet V2
-  Metadata:
-    FLOPs: 319000000
-    Parameters: 3500000
-  Name: mobilenet_v2_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 71.86
-      Top 5 Accuracy: 90.42
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth
+  - Name: mobilenet_v2_b32x8_imagenet
+    Metadata:
+      FLOPs: 319000000
+      Parameters: 3500000
+    In Collection: MobileNet V2
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 71.86
+          Top 5 Accuracy: 90.42
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth
+    Config: configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py
--- a/configs/mobilenet_v3/metafile.yml
+++ b/configs/mobilenet_v3/metafile.yml
@ -0,0 +1,42 @@
+Collections:
+  - Name: MobileNet V3
+    Metadata:
+      Training Data: ImageNet-1k
+      Training Techniques:
+        - RMSprop with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Epochs: 600
+      Batch Size: 1024
+      Architecture:
+        - MobileNet V3
+    Paper: https://arxiv.org/abs/1905.02244
+    README: configs/mobilenet_v3/README.md
+
+Models:
+  - Name: mobilenet_v3_small_imagenet
+    Metadata:
+      FLOPs: 60000000
+      Parameters: 2540000
+    In Collection: MobileNet V3
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 67.66
+          Top 5 Accuracy: 87.41
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth
+    Config: configs/mobilenet_v3/mobilenet_v3_small_imagenet.py
+  - Name: mobilenet_v3_large_imagenet
+    Metadata:
+      FLOPs: 230000000
+      Parameters: 5480000
+    In Collection: MobileNet V3
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 74.04
+          Top 5 Accuracy: 91.34
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_large-3ea3c186.pth
+    Config: configs/mobilenet_v3/mobilenet_v3_large_imagenet.py
--- a/configs/resnet/metafile.yml
+++ b/configs/resnet/metafile.yml
@ -1,7 +1,7 @@
 Collections:
  - Name: ResNet
    Metadata:
-      Training Data: ImageNet
+      Training Data: ImageNet-1k
      Training Techniques:
        - SGD with Momentum
        - Weight Decay
@ -14,204 +14,204 @@ Collections:
    README: configs/resnet/README.md

 Models:
- Config: configs/resnet/resnet18_b16x8_cifar10.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 560000000
-    Parameters: 11170000
-    Training Data: CIFAR-10
-    Training Resources: 8x 1080 GPUs
-    Epochs: 200
-    Batch Size: 128
-  Name: resnet18_b16x8_cifar10
-  Results:
-  - Dataset: CIFAR-10
-    Metrics:
-      Top 1 Accuracy: 94.72
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20200823-f906fa4e.pth
- Config: configs/resnet/resnet34_b16x8_cifar10.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 1160000000
-    Parameters: 21280000
-    Training Data: CIFAR-10
-    Training Resources: 8x 1080 GPUs
-    Epochs: 200
-    Batch Size: 128
-  Name: resnet34_b16x8_cifar10
-  Results:
-  - Dataset: CIFAR-10
-    Metrics:
-      Top 1 Accuracy: 95.34
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20200823-52d5d832.pth
- Config: configs/resnet/resnet50_b16x8_cifar10.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 1310000000
-    Parameters: 23520000
-    Training Data: CIFAR-10
-    Training Resources: 8x 1080 GPUs
-    Epochs: 200
-    Batch Size: 128
-  Name: resnet50_b16x8_cifar10
-  Results:
-  - Dataset: CIFAR-10
-    Metrics:
-      Top 1 Accuracy: 95.36
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20200823-882aa7b1.pth
- Config: configs/resnet/resnet101_b16x8_cifar10.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 2520000000
-    Parameters: 42510000
-    Training Data: CIFAR-10
-    Training Resources: 8x 1080 GPUs
-    Epochs: 200
-    Batch Size: 128
-  Name: resnet101_b16x8_cifar10
-  Results:
-  - Dataset: CIFAR-10
-    Metrics:
-      Top 1 Accuracy: 95.66
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20200823-d9501bbc.pth
- Config: configs/resnet/resnet152_b16x8_cifar10.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 3740000000
-    Parameters: 58160000
-    Training Data: CIFAR-10
-    Training Resources: 8x 1080 GPUs
-    Epochs: 200
-    Batch Size: 128
-  Name: resnet152_b16x8_cifar10
-  Results:
-  - Dataset: CIFAR-10
-    Metrics:
-      Top 1 Accuracy: 95.96
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20200823-ad4d5d0c.pth
- Config: configs/resnet/resnet50_b16x8_cifar100.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 1310000000
-    Parameters: 23710000
-    Training Data: CIFAR-100
-    Training Resources: 8x 1080 GPUs
-    Epochs: 200
-    Batch Size: 128
-  Name: resnet50_b16x8_cifar100
-  Results:
-  - Dataset: CIFAR-100
-    Metrics:
-      Top 1 Accuracy: 80.51
-      Top 5 Accuracy: 95.27
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_cifar100_20210410-37f13c16.pth
- Config: configs/resnet/resnet18_b32x8_imagenet.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 1820000000
-    Parameters: 11690000
-  Name: resnet18_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 70.07
-      Top 5 Accuracy: 89.44
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.pth
- Config: configs/resnet/resnet34_b32x8_imagenet.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 3680000000
-    Parameters: 2180000
-  Name: resnet34_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 73.85
-      Top 5 Accuracy: 91.53
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth
- Config: configs/resnet/resnet50_b32x8_imagenet.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 4120000000
-    Parameters: 25560000
-  Name: resnet50_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 76.55
-      Top 5 Accuracy: 93.15
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth
- Config: configs/resnet/resnet101_b32x8_imagenet.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 7850000000
-    Parameters: 44550000
-  Name: resnet101_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 78.18
-      Top 5 Accuracy: 94.03
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.pth
- Config: configs/resnet/resnet152_b32x8_imagenet.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 11580000000
-    Parameters: 60190000
-  Name: resnet152_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 78.63
-      Top 5 Accuracy: 94.16
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.pth
- Config: configs/resnet/resnetv1d50_b32x8_imagenet.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 4360000000
-    Parameters: 25580000
-  Name: resnetv1d50_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 77.4
-      Top 5 Accuracy: 93.66
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_batch256_imagenet_20200708-1ad0ce94.pth
- Config: configs/resnet/resnetv1d101_b32x8_imagenet.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 8090000000
-    Parameters: 44570000
-  Name: resnetv1d101_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 78.85
-      Top 5 Accuracy: 94.38
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_batch256_imagenet_20200708-9cb302ef.pth
- Config: configs/resnet/resnetv1d152_b32x8_imagenet.py
-  In Collection: ResNet
-  Metadata:
-    FLOPs: 11820000000
-    Parameters: 60210000
-  Name: resnetv1d152_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 79.35
-      Top 5 Accuracy: 94.61
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_batch256_imagenet_20200708-e79cb6a2.pth
+  - Name: resnet18_b16x8_cifar10
+    Metadata:
+      FLOPs: 560000000
+      Parameters: 11170000
+      Training Data: CIFAR-10
+      Training Resources: 8x 1080 GPUs
+      Epochs: 200
+      Batch Size: 128
+    In Collection: ResNet
+    Results:
+      - Dataset: CIFAR-10
+        Metrics:
+          Top 1 Accuracy: 94.72
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20200823-f906fa4e.pth
+    Config: configs/resnet/resnet18_b16x8_cifar10.py
+  - Name: resnet34_b16x8_cifar10
+    Metadata:
+      FLOPs: 1160000000
+      Parameters: 21280000
+      Training Data: CIFAR-10
+      Training Resources: 8x 1080 GPUs
+      Epochs: 200
+      Batch Size: 128
+    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_20200823-52d5d832.pth
+    Config: configs/resnet/resnet34_b16x8_cifar10.py
+  - Name: resnet50_b16x8_cifar10
+    Metadata:
+      FLOPs: 1310000000
+      Parameters: 23520000
+      Training Data: CIFAR-10
+      Training Resources: 8x 1080 GPUs
+      Epochs: 200
+      Batch Size: 128
+    In Collection: ResNet
+    Results:
+      - Dataset: CIFAR-10
+        Metrics:
+          Top 1 Accuracy: 95.36
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20200823-882aa7b1.pth
+    Config: configs/resnet/resnet50_b16x8_cifar10.py
+  - Name: resnet101_b16x8_cifar10
+    Metadata:
+      FLOPs: 2520000000
+      Parameters: 42510000
+      Training Data: CIFAR-10
+      Training Resources: 8x 1080 GPUs
+      Epochs: 200
+      Batch Size: 128
+    In Collection: ResNet
+    Results:
+      - Dataset: CIFAR-10
+        Metrics:
+          Top 1 Accuracy: 95.66
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20200823-d9501bbc.pth
+    Config: configs/resnet/resnet101_b16x8_cifar10.py
+  - Name: resnet152_b16x8_cifar10
+    Metadata:
+      FLOPs: 3740000000
+      Parameters: 58160000
+      Training Data: CIFAR-10
+      Training Resources: 8x 1080 GPUs
+      Epochs: 200
+      Batch Size: 128
+    In Collection: ResNet
+    Results:
+      - Dataset: CIFAR-10
+        Metrics:
+          Top 1 Accuracy: 95.96
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20200823-ad4d5d0c.pth
+    Config: configs/resnet/resnet152_b16x8_cifar10.py
+  - Name: resnet50_b16x8_cifar100
+    Metadata:
+      FLOPs: 1310000000
+      Parameters: 23710000
+      Training Data: CIFAR-100
+      Training Resources: 8x 1080 GPUs
+      Epochs: 200
+      Batch Size: 128
+    In Collection: ResNet
+    Results:
+      - Dataset: CIFAR-100
+        Metrics:
+          Top 1 Accuracy: 80.51
+          Top 5 Accuracy: 95.27
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_cifar100_20210410-37f13c16.pth
+    Config: configs/resnet/resnet50_b16x8_cifar100.py
+  - Name: resnet18_b32x8_imagenet
+    Metadata:
+      FLOPs: 1820000000
+      Parameters: 11690000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 70.07
+          Top 5 Accuracy: 89.44
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.pth
+    Config: configs/resnet/resnet18_b32x8_imagenet.py
+  - Name: resnet34_b32x8_imagenet
+    Metadata:
+      FLOPs: 3680000000
+      Parameters: 2180000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 73.85
+          Top 5 Accuracy: 91.53
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth
+    Config: configs/resnet/resnet34_b32x8_imagenet.py
+  - Name: resnet50_b32x8_imagenet
+    Metadata:
+      FLOPs: 4120000000
+      Parameters: 25560000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 76.55
+          Top 5 Accuracy: 93.15
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth
+    Config: configs/resnet/resnet50_b32x8_imagenet.py
+  - Name: resnet101_b32x8_imagenet
+    Metadata:
+      FLOPs: 7850000000
+      Parameters: 44550000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.18
+          Top 5 Accuracy: 94.03
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.pth
+    Config: configs/resnet/resnet101_b32x8_imagenet.py
+  - Name: resnet152_b32x8_imagenet
+    Metadata:
+      FLOPs: 11580000000
+      Parameters: 60190000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.63
+          Top 5 Accuracy: 94.16
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.pth
+    Config: configs/resnet/resnet152_b32x8_imagenet.py
+  - Name: resnetv1d50_b32x8_imagenet
+    Metadata:
+      FLOPs: 4360000000
+      Parameters: 25580000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 77.4
+          Top 5 Accuracy: 93.66
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_batch256_imagenet_20200708-1ad0ce94.pth
+    Config: configs/resnet/resnetv1d50_b32x8_imagenet.py
+  - Name: resnetv1d101_b32x8_imagenet
+    Metadata:
+      FLOPs: 8090000000
+      Parameters: 44570000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.85
+          Top 5 Accuracy: 94.38
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_batch256_imagenet_20200708-9cb302ef.pth
+    Config: configs/resnet/resnetv1d101_b32x8_imagenet.py
+  - Name: resnetv1d152_b32x8_imagenet
+    Metadata:
+      FLOPs: 11820000000
+      Parameters: 60210000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 79.35
+          Top 5 Accuracy: 94.61
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_batch256_imagenet_20200708-e79cb6a2.pth
+    Config: configs/resnet/resnetv1d152_b32x8_imagenet.py
--- a/configs/resnext/metafile.yml
+++ b/configs/resnext/metafile.yml
@ -1,7 +1,7 @@
 Collections:
  - Name: ResNeXt
    Metadata:
-      Training Data: ImageNet
+      Training Data: ImageNet-1k
      Training Techniques:
        - SGD with Momentum
        - Weight Decay
@ -14,55 +14,55 @@ Collections:
    README: configs/resnext/README.md

 Models:
- Config: configs/resnext/resnext50_32x4d_b32x8_imagenet.py
-  In Collection: ResNeXt
-  Metadata:
-    FLOPs: 4270000000
-    Parameters: 25030000
-  Name: resnext50_32x4d_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 77.92
-      Top 5 Accuracy: 93.74
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_batch256_imagenet_20200708-c07adbb7.pth
- Config: configs/resnext/resnext101_32x4d_b32x8_imagenet.py
-  In Collection: ResNeXt
-  Metadata:
-    FLOPs: 8030000000
-    Parameters: 44180000
-  Name: resnext101_32x4d_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 78.7
-      Top 5 Accuracy: 94.34
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_batch256_imagenet_20200708-87f2d1c9.pth
- Config: configs/resnext/resnext101_32x8d_b32x8_imagenet.py
-  In Collection: ResNeXt
-  Metadata:
-    FLOPs: 16500000000
-    Parameters: 88790000
-  Name: resnext101_32x8d_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 79.22
-      Top 5 Accuracy: 94.52
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_batch256_imagenet_20200708-1ec34aa7.pth
- Config: configs/resnext/resnext152_32x4d_b32x8_imagenet.py
-  In Collection: ResNeXt
-  Metadata:
-    FLOPs: 11800000000
-    Parameters: 59950000
-  Name: resnext152_32x4d_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 79.06
-      Top 5 Accuracy: 94.47
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_batch256_imagenet_20200708-aab5034c.pth
+  - Name: resnext50_32x4d_b32x8_imagenet
+    Metadata:
+      FLOPs: 4270000000
+      Parameters: 25030000
+    In Collection: ResNeXt
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 77.92
+          Top 5 Accuracy: 93.74
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_batch256_imagenet_20200708-c07adbb7.pth
+    Config: configs/resnext/resnext50_32x4d_b32x8_imagenet.py
+  - Name: resnext101_32x4d_b32x8_imagenet
+    Metadata:
+      FLOPs: 8030000000
+      Parameters: 44180000
+    In Collection: ResNeXt
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.7
+          Top 5 Accuracy: 94.34
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_batch256_imagenet_20200708-87f2d1c9.pth
+    Config: configs/resnext/resnext101_32x4d_b32x8_imagenet.py
+  - Name: resnext101_32x8d_b32x8_imagenet
+    Metadata:
+      FLOPs: 16500000000
+      Parameters: 88790000
+    In Collection: ResNeXt
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 79.22
+          Top 5 Accuracy: 94.52
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_batch256_imagenet_20200708-1ec34aa7.pth
+    Config: configs/resnext/resnext101_32x8d_b32x8_imagenet.py
+  - Name: resnext152_32x4d_b32x8_imagenet
+    Metadata:
+      FLOPs: 11800000000
+      Parameters: 59950000
+    In Collection: ResNeXt
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 79.06
+          Top 5 Accuracy: 94.47
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_batch256_imagenet_20200708-aab5034c.pth
+    Config: configs/resnext/resnext152_32x4d_b32x8_imagenet.py
--- a/configs/seresnet/metafile.yml
+++ b/configs/seresnet/metafile.yml
@ -1,7 +1,7 @@
 Collections:
  - Name: SEResNet
    Metadata:
-      Training Data: ImageNet
+      Training Data: ImageNet-1k
      Training Techniques:
        - SGD with Momentum
        - Weight Decay
@ -14,29 +14,29 @@ Collections:
    README: configs/seresnet/README.md

 Models:
- Config: configs/seresnet50/seresnet50_b32x8_imagenet.py
-  In Collection: SEResNet
-  Metadata:
-    FLOPs: 4130000000
-    Parameters: 28090000
-  Name: seresnet50_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 77.74
-      Top 5 Accuracy: 93.84
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth
- Config: configs/seresnet101/seresnet101_b32x8_imagenet.py
-  In Collection: SEResNet
-  Metadata:
-    FLOPs: 7860000000
-    Parameters: 49330000
-  Name: seresnet101_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 78.26
-      Top 5 Accuracy: 94.07
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth
+  - Name: seresnet50_b32x8_imagenet
+    Metadata:
+      FLOPs: 4130000000
+      Parameters: 28090000
+    In Collection: SEResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 77.74
+          Top 5 Accuracy: 93.84
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth
+    Config: configs/seresnet50/seresnet50_b32x8_imagenet.py
+  - Name: seresnet101_b32x8_imagenet
+    Metadata:
+      FLOPs: 7860000000
+      Parameters: 49330000
+    In Collection: SEResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.26
+          Top 5 Accuracy: 94.07
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth
+    Config: configs/seresnet101/seresnet101_b32x8_imagenet.py
--- a/configs/shufflenet_v1/metafile.yml
+++ b/configs/shufflenet_v1/metafile.yml
@ -1,7 +1,7 @@
 Collections:
  - Name: Shufflenet V1
    Metadata:
-      Training Data: ImageNet
+      Training Data: ImageNet-1k
      Training Techniques:
        - SGD with Momentum
        - Weight Decay
@ -15,16 +15,16 @@ Collections:
    README: configs/shufflenet_v1/README.md

 Models:
- Config: configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py
-  In Collection: Shufflenet V1
-  Metadata:
-    FLOPs: 146000000
-    Parameters: 1870000
-  Name: shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 68.13
-      Top 5 Accuracy: 87.81
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth
+  - Name: shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet
+    Metadata:
+      FLOPs: 146000000
+      Parameters: 1870000
+    In Collection: Shufflenet V1
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 68.13
+          Top 5 Accuracy: 87.81
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth
+    Config: configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py
--- a/configs/shufflenet_v2/metafile.yml
+++ b/configs/shufflenet_v2/metafile.yml
@ -1,7 +1,7 @@
 Collections:
  - Name: Shufflenet V2
    Metadata:
-      Training Data: ImageNet
+      Training Data: ImageNet-1k
      Training Techniques:
        - SGD with Momentum
        - Weight Decay
@ -15,16 +15,16 @@ Collections:
    README: configs/shufflenet_v2/README.md

 Models:
- Config: configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py
-  In Collection: Shufflenet V2
-  Metadata:
-    FLOPs: 149000000
-    Parameters: 2280000
-  Name: shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 69.55
-      Top 5 Accuracy: 88.92
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth
+  - Name: shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet
+    Metadata:
+      FLOPs: 149000000
+      Parameters: 2280000
+    In Collection: Shufflenet V2
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 69.55
+          Top 5 Accuracy: 88.92
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth
+    Config: configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py
--- a/configs/vgg/metafile.yml
+++ b/configs/vgg/metafile.yml
@ -1,7 +1,7 @@
 Collections:
  - Name: VGG
    Metadata:
-      Training Data: ImageNet
+      Training Data: ImageNet-1k
      Training Techniques:
        - SGD with Momentum
        - Weight Decay
@ -14,107 +14,107 @@ Collections:
    README: configs/vgg/README.md

 Models:
- Config: configs/vgg/vgg11_b32x8_imagenet.py
-  In Collection: VGG
-  Metadata:
-    FLOPs: 7630000000
-    Parameters: 132860000
-  Name: vgg11_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 68.75
-      Top 5 Accuracy: 88.87
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth
- Config: configs/vgg/vgg13_b32x8_imagenet.py
-  In Collection: VGG
-  Metadata:
-    FLOPs: 11340000000
-    Parameters: 133050000
-  Name: vgg13_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 70.02
-      Top 5 Accuracy: 89.46
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth
- Config: configs/vgg/vgg16_b32x8_imagenet.py
-  In Collection: VGG
-  Metadata:
-    FLOPs: 15500000000
-    Parameters: 138360000
-  Name: vgg16_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 71.62
-      Top 5 Accuracy: 90.49
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth
- Config: configs/vgg/vgg19_b32x8_imagenet.py
-  In Collection: VGG
-  Metadata:
-    FLOPs: 19670000000
-    Parameters: 143670000
-  Name: vgg19_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 72.41
-      Top 5 Accuracy: 90.8
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth
- Config: configs/vgg/vgg11bn_b32x8_imagenet.py
-  In Collection: VGG
-  Metadata:
-    FLOPs: 7640000000
-    Parameters: 132870000
-  Name: vgg11bn_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 70.75
-      Top 5 Accuracy: 90.12
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth
- Config: configs/vgg/vgg13bn_b32x8_imagenet.py
-  In Collection: VGG
-  Metadata:
-    FLOPs: 11360000000
-    Parameters: 133050000
-  Name: vgg13bn_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 72.15
-      Top 5 Accuracy: 90.71
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth
- Config: configs/vgg/vgg16_b32x8_imagenet.py
-  In Collection: VGG
-  Metadata:
-    FLOPs: 15530000000
-    Parameters: 138370000
-  Name: vgg16_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 73.72
-      Top 5 Accuracy: 91.68
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth
- Config: configs/vgg/vgg19bn_b32x8_imagenet.py
-  In Collection: VGG
-  Metadata:
-    FLOPs: 19700000000
-    Parameters: 143680000
-  Name: vgg19bn_b32x8_imagenet
-  Results:
-  - Dataset: ImageNet
-    Metrics:
-      Top 1 Accuracy: 74.7
-      Top 5 Accuracy: 92.24
-    Task: Image Classification
-  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth
+  - Name: vgg11_b32x8_imagenet
+    Metadata:
+      FLOPs: 7630000000
+      Parameters: 132860000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 68.75
+          Top 5 Accuracy: 88.87
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth
+    Config: configs/vgg/vgg11_b32x8_imagenet.py
+  - Name: vgg13_b32x8_imagenet
+    Metadata:
+      FLOPs: 11340000000
+      Parameters: 133050000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 70.02
+          Top 5 Accuracy: 89.46
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth
+    Config: configs/vgg/vgg13_b32x8_imagenet.py
+  - Name: vgg16_b32x8_imagenet
+    Metadata:
+      FLOPs: 15500000000
+      Parameters: 138360000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 71.62
+          Top 5 Accuracy: 90.49
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth
+    Config: configs/vgg/vgg16_b32x8_imagenet.py
+  - Name: vgg19_b32x8_imagenet
+    Metadata:
+      FLOPs: 19670000000
+      Parameters: 143670000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 72.41
+          Top 5 Accuracy: 90.8
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth
+    Config: configs/vgg/vgg19_b32x8_imagenet.py
+  - Name: vgg11bn_b32x8_imagenet
+    Metadata:
+      FLOPs: 7640000000
+      Parameters: 132870000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 70.75
+          Top 5 Accuracy: 90.12
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth
+    Config: configs/vgg/vgg11bn_b32x8_imagenet.py
+  - Name: vgg13bn_b32x8_imagenet
+    Metadata:
+      FLOPs: 11360000000
+      Parameters: 133050000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 72.15
+          Top 5 Accuracy: 90.71
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth
+    Config: configs/vgg/vgg13bn_b32x8_imagenet.py
+  - Name: vgg16_b32x8_imagenet
+    Metadata:
+      FLOPs: 15530000000
+      Parameters: 138370000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 73.72
+          Top 5 Accuracy: 91.68
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth
+    Config: configs/vgg/vgg16_b32x8_imagenet.py
+  - Name: vgg19bn_b32x8_imagenet
+    Metadata:
+      FLOPs: 19700000000
+      Parameters: 143680000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 74.7
+          Top 5 Accuracy: 92.24
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth
+    Config: configs/vgg/vgg19bn_b32x8_imagenet.py
--- a/model-index.yml
+++ b/model-index.yml