[Refactor] refactor Metafile format (#478)

* update metafile * update format * update metafile * update mae
open-mmlab · Oct 12, 2022 · 337e49e · 337e49e
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Showing 18 changed files with 541 additions and 160 deletions.
diff --git a/configs/selfsup/barlowtwins/metafile.yml b/configs/selfsup/barlowtwins/metafile.yml
@@ -19,10 +19,18 @@ Models:
     Metadata:
       Epochs: 300
       Batch Size: 2048
-    Results:
-      - Task: Self-Supervised Image Classification
-        Dataset: ImageNet-1k
-        Metrics:
-          Top 1 Accuracy: 71.66
+    Results: null
     Config: configs/selfsup/barlowtwins/barlowtwins_resnet50_8xb256-coslr-300e_in1k.py
-    Weights: https://download.openmmlab.com/mmselfsup/barlowtwins/barlowtwins_resnet50_8xb256-coslr-300e_in1k_20220419-5ae15f89.pth
+    Weights: https://download.openmmlab.com/mmselfsup/1.x/barlowtwins/barlowtwins_resnet50_8xb256-coslr-300e_in1k/barlowtwins_resnet50_8xb256-coslr-300e_in1k_20220825-57307488.pth
+    Downstream:
+      - Type: Image Classification
+        Metadata:
+          Epochs: 100
+          Batch Size: 256
+        Results:
+          - Task: Linear Evaluation
+            Dataset: ImageNet-1k
+            Metrics:
+              Top 1 Accuracy: 71.8
+        Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-coslr-100e_in1k.py
+        Weights: https://download.openmmlab.com/mmselfsup/1.x/barlowtwins/barlowtwins_resnet50_8xb256-coslr-300e_in1k/resnet50_linear-8xb32-coslr-100e_in1k/resnet50_linear-8xb32-coslr-100e_in1k_20220825-52fde35f.pth
diff --git a/configs/selfsup/byol/metafile.yml b/configs/selfsup/byol/metafile.yml
@@ -14,39 +14,23 @@ Collections:
     README: configs/selfsup/byol/README.md
 
 Models:
-  - Name: byol_resnet50_8xb32-accum16-coslr-200e_in1k
-    In Collection: BYOL
-    Metadata:
-      Epochs: 200
-      Batch Size: 256
-    Results:
-      - Task: Self-Supervised Image Classification
-        Dataset: ImageNet-1k
-        Metrics:
-          Top 1 Accuracy: 71.72
-    Config: configs/selfsup/byol/byol_resnet50_8xb32-accum16-coslr-200e_in1k.py
-    Weights: https://download.openmmlab.com/mmselfsup/byol/byol_resnet50_8xb32-accum16-coslr-200e_in1k_20220225-5c8b2c2e.pth
   - Name: byol_resnet50_16xb256-coslr-200e_in1k
     In Collection: BYOL
     Metadata:
       Epochs: 200
       Batch Size: 4096
-    Results:
-      - Task: Self-Supervised Image Classification
-        Dataset: ImageNet-1k
-        Metrics:
-          Top 1 Accuracy: 71.88
+    Results: null
     Config: configs/selfsup/byol/byol_resnet50_16xb256-coslr-200e_in1k.py
-    Weights: https://download.openmmlab.com/mmselfsup/byol/byol_resnet50_16xb256-coslr-200e_in1k_20220527-b6f8eedd.pth
-  - Name: byol_resnet50_8xb32-accum16-coslr-300e_in1k
-    In Collection: BYOL
-    Metadata:
-      Epochs: 300
-      Batch Size: 256
-    Results:
-      - Task: Self-Supervised Image Classification
-        Dataset: ImageNet-1k
-        Metrics:
-          Top 1 Accuracy: 72.93
-    Config: configs/selfsup/byol/byol_resnet50_8xb32-accum16-coslr-300e_in1k.py
-    Weights: https://download.openmmlab.com/mmselfsup/byol/byol_resnet50_8xb32-accum16-coslr-300e_in1k_20220225-a0daa54a.pth
+    Weights: https://download.openmmlab.com/mmselfsup/1.x/byol/byol_resnet50_16xb256-coslr-200e_in1k/byol_resnet50_16xb256-coslr-200e_in1k_20220825-de817331.pth
+    Downstream:
+      - Type: Image Classification
+        Metadata:
+          Epochs: 90
+          Batch Size: 4096
+        Results:
+          - Task: Linear Evaluation
+            Dataset: ImageNet-1k
+            Metrics:
+              Top 1 Accuracy: 71.8
+        Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb512-coslr-90e_in1k.py
+        Weights: https://download.openmmlab.com/mmselfsup/1.x/byol/byol_resnet50_16xb256-coslr-200e_in1k/resnet50_linear-8xb512-coslr-90e_in1k/resnet50_linear-8xb512-coslr-90e_in1k_20220825-7596c6f5.pth
diff --git a/configs/selfsup/cae/RAEDME.md → configs/selfsup/cae/README.md b/configs/selfsup/cae/RAEDME.md → configs/selfsup/cae/README.md
diff --git a/configs/selfsup/cae/metafile.yaml b/configs/selfsup/cae/metafile.yaml
diff --git a/configs/selfsup/cae/metafile.yml b/configs/selfsup/cae/metafile.yml
@@ -0,0 +1,35 @@
+Collections:
+  - Name: CAE
+    Metadata:
+      Training Data: ImageNet-1k
+      Training Techniques:
+        - AdamW
+      Training Resources: 16x A100-80G GPUs
+      Architecture:
+        - ViT
+    Paper:
+        URL: https://arxiv.org/abs/2202.03026
+        Title: "Context Autoencoder for Self-Supervised Representation Learning"
+    README: configs/selfsup/cae/README.md
+
+Models:
+  - Name: cae_vit-base-p16_8xb256-fp16-coslr-300e_in1k
+    In Collection: CAE
+    Metadata:
+      Epochs: 300
+      Batch Size: 2048
+    Results: null
+    Config: configs/selfsup/cae/cae_vit-base-p16_16xb128-amp-coslr-300e_in1k.py
+    Weights: https://download.openmmlab.com/mmselfsup/1.x/cae/cae_vit-base-p16_16xb128-fp16-coslr-300e_in1k/cae_vit-base-p16_16xb128-fp16-coslr-300e_in1k_20220825-404a1929.pth
+    Downstream:
+      - Type: Image Classification
+        Metadata:
+          Epochs: 100
+          Batch Size: 1024
+        Results:
+          - Task: Fine-tuning
+            Dataset: ImageNet-1k
+            Metrics:
+              Top 1 Accuracy: 60.8
+        Config: configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb128-coslr-100e-rpe_in1k.py
+        Weights: https://download.openmmlab.com/mmselfsup/1.x/cae/cae_vit-base-p16_16xb128-fp16-coslr-300e_in1k/vit-base-p16_ft-8xb128-coslr-100e-rpe_in1k/vit-base-p16_ft-8xb128-coslr-100e-rpe_in1k_20220825-f3d234cd.pth
diff --git a/configs/selfsup/densecl/metafile.yml b/configs/selfsup/densecl/metafile.yml
@@ -19,10 +19,18 @@ Models:
     Metadata:
       Epochs: 200
       Batch Size: 256
-    Results:
-      - Task: Self-Supervised Image Classification
-        Dataset: ImageNet-1k
-        Metrics:
-          Top 1 Accuracy: 63.62
+    Results: null
     Config: configs/selfsup/densecl/densecl_resnet50_8xb32-coslr-200e_in1k.py
-    Weights: https://download.openmmlab.com/mmselfsup/densecl/densecl_resnet50_8xb32-coslr-200e_in1k_20220225-8c7808fe.pth
+    Weights: https://download.openmmlab.com/mmselfsup/1.x/densecl/densecl_resnet50_8xb32-coslr-200e_in1k/densecl_resnet50_8xb32-coslr-200e_in1k_20220825-3078723b.pth
+    Downstream:
+      - Type: Image Classification
+        Metadata:
+          Epochs: 100
+          Batch Size: 256
+        Results:
+          - Task: Linear Evaluation
+            Dataset: ImageNet-1k
+            Metrics:
+              Top 1 Accuracy: 63.5
+        Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py
+        Weights: https://download.openmmlab.com/mmselfsup/1.x/densecl/densecl_resnet50_8xb32-coslr-200e_in1k/resnet50_linear-8xb32-steplr-100e_in1k/resnet50_linear-8xb32-steplr-100e_in1k_20220825-f0f0a579.pth
diff --git a/configs/selfsup/mae/metafile.yaml b/configs/selfsup/mae/metafile.yaml