diff --git a/configs/selfsup/barlowtwins/metafile.yml b/configs/selfsup/barlowtwins/metafile.yml
index ddb44060..139a9b3d 100644
--- 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
index d173da40..c1288696 100644
--- 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 b/configs/selfsup/cae/README.md
similarity index 100%
rename from configs/selfsup/cae/RAEDME.md
rename to configs/selfsup/cae/README.md
diff --git a/configs/selfsup/cae/metafile.yaml b/configs/selfsup/cae/metafile.yaml
deleted file mode 100644
index 627153ef..00000000
--- a/configs/selfsup/cae/metafile.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-Collections:
- - Name: CAE
- Metadata:
- Training Data: ImageNet-1k
- Training Techniques:
- - AdamW
- Training Resources: 8x 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:
- - Task: Self-Supervised Image Classification
- Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 83.2
- Config: configs/selfsup/cae/cae_vit-base-p16_8xb256-fp16-coslr-300e_in1k.py
- Weights: https://download.openmmlab.com/mmselfsup/cae/cae_vit-base-p16_16xb256-coslr-300e_in1k-224_20220427-4c786349.pth
diff --git a/configs/selfsup/cae/metafile.yml b/configs/selfsup/cae/metafile.yml
new file mode 100644
index 00000000..78c3c48a
--- /dev/null
+++ 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
index ad1f0eed..0dcd1d22 100644
--- 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
deleted file mode 100644
index dc0773b0..00000000
--- a/configs/selfsup/mae/metafile.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-Collections:
- - Name: MAE
- Metadata:
- Training Data: ImageNet-1k
- Training Techniques:
- - AdamW
- Training Resources: 8x A100-80G GPUs
- Architecture:
- - ViT
- Paper:
- URL: https://arxiv.org/abs/2111.06377
- Title: "Masked Autoencoders Are Scalable Vision Learners"
- README: configs/selfsup/mae/README.md
-
-Models:
- - Name: mae_vit-base-p16_8xb512-coslr-400e_in1k
- In Collection: MAE
- Metadata:
- Epochs: 400
- Batch Size: 4096
- Results:
- - Task: Self-Supervised Image Classification
- Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 83.1
- Config: configs/selfsup/mae/mae_vit-base-p16_8xb512-coslr-400e_in1k.py
- Weights: https://download.openmmlab.com/mmselfsup/mae/mae_vit-base-p16_8xb512-coslr-400e_in1k-224_20220223-85be947b.pth
diff --git a/configs/selfsup/mae/metafile.yml b/configs/selfsup/mae/metafile.yml
new file mode 100644
index 00000000..5de231c7
--- /dev/null
+++ b/configs/selfsup/mae/metafile.yml
@@ -0,0 +1,250 @@
+Collections:
+ - Name: MAE
+ Metadata:
+ Training Data: ImageNet-1k
+ Training Techniques:
+ - AdamW
+ Training Resources: 8x A100-80G GPUs
+ Architecture:
+ - ViT
+ Paper:
+ URL: https://arxiv.org/abs/2111.06377
+ Title: "Masked Autoencoders Are Scalable Vision Learners"
+ README: configs/selfsup/mae/README.md
+
+Models:
+ - Name: mae_vit-base-p16_8xb512-amp-coslr-300e_in1k
+ In Collection: MAE
+ Metadata:
+ Epochs: 300
+ Batch Size: 4096
+ Results: null
+ Config: configs/selfsup/mae/mae_vit-base-p16_8xb512-amp-coslr-300e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-300e_in1k/mae_vit-base-p16_8xb512-coslr-300e-fp16_in1k_20220829-c2cf66ba.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 90
+ Batch Size: 16384
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 60.8
+ Config: configs/benchmarks/classification/imagenet/vit-base-p16_linear-8xb2048-coslr-90e_in1k.py
+ - Type: Image Classification
+ Metadata:
+ Epochs: 100
+ Batch Size: 1024
+ Results:
+ - Task: Fine-tuning
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 83.1
+ Config: configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb128-coslr-100e_in1k.py
+ - Name: mae_vit-base-p16_8xb512-amp-coslr-400e_in1k
+ In Collection: MAE
+ Metadata:
+ Epochs: 400
+ Batch Size: 4096
+ Results: null
+ Config: configs/selfsup/mae/mae_vit-base-p16_8xb512-amp-coslr-400e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-400e_in1k/mae_vit-base-p16_8xb512-coslr-400e-fp16_in1k_20220825-bc79e40b.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 90
+ Batch Size: 16384
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 62.5
+ Config: configs/benchmarks/classification/imagenet/vit-base-p16_linear-8xb2048-coslr-90e_in1k.py
+ - Type: Image Classification
+ Metadata:
+ Epochs: 100
+ Batch Size: 1024
+ Results:
+ - Task: Fine-tuning
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 83.3
+ Config: configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb128-coslr-100e_in1k.py
+ - Name: mae_vit-base-p16_8xb512-amp-coslr-800e_in1k
+ In Collection: MAE
+ Metadata:
+ Epochs: 800
+ Batch Size: 4096
+ Results: null
+ Config: configs/selfsup/mae/mae_vit-base-p16_8xb512-amp-coslr-800e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-800e_in1k/mae_vit-base-p16_8xb512-coslr-800e-fp16_in1k_20220825-5d81fbc4.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 90
+ Batch Size: 16384
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 65.1
+ Config: configs/benchmarks/classification/imagenet/vit-base-p16_linear-8xb2048-coslr-90e_in1k.py
+ - Type: Image Classification
+ Metadata:
+ Epochs: 100
+ Batch Size: 1024
+ Results:
+ - Task: Fine-tuning
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 83.3
+ Config: configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb128-coslr-100e_in1k.py
+ - Name: mae_vit-base-p16_8xb512-amp-coslr-1600e_in1k
+ In Collection: MAE
+ Metadata:
+ Epochs: 1600
+ Batch Size: 4096
+ Results: null
+ Config: configs/selfsup/mae/mae_vit-base-p16_8xb512-amp-coslr-1600e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-1600e_in1k/mae_vit-base-p16_8xb512-fp16-coslr-1600e_in1k_20220825-f7569ca2.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 90
+ Batch Size: 16384
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 67.1
+ Config: configs/benchmarks/classification/imagenet/vit-base-p16_linear-8xb2048-coslr-90e_in1k.py
+ - Type: Image Classification
+ Metadata:
+ Epochs: 100
+ Batch Size: 1024
+ Results:
+ - Task: Fine-tuning
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 83.5
+ Config: configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb128-coslr-100e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-1600e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k_20220825-cf70aa21.pth
+ - Name: mae_vit-large-p16_8xb512-amp-coslr-400e_in1k
+ In Collection: MAE
+ Metadata:
+ Epochs: 400
+ Batch Size: 4096
+ Results: null
+ Config: configs/selfsup/mae/mae_vit-large-p16_8xb512-amp-coslr-400e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-large-p16_8xb512-fp16-coslr-400e_in1k/mae_vit-large-p16_8xb512-fp16-coslr-400e_in1k_20220825-b11d0425.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 90
+ Batch Size: 16384
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 70.7
+ Config: configs/benchmarks/classification/imagenet/vit-large-p16_linear-8xb2048-coslr-90e_in1k.py
+ - Type: Image Classification
+ Metadata:
+ Epochs: 50
+ Batch Size: 1024
+ Results:
+ - Task: Fine-tuning
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 85.2
+ Config: configs/benchmarks/classification/imagenet/vit-large-p16_ft-8xb128-coslr-50e_in1k.py
+ - Name: mae_vit-large-p16_8xb512-amp-coslr-800e_in1k
+ In Collection: MAE
+ Metadata:
+ Epochs: 800
+ Batch Size: 4096
+ Results: null
+ Config: configs/selfsup/mae/mae_vit-large-p16_8xb512-amp-coslr-800e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-large-p16_8xb512-fp16-coslr-800e_in1k/mae_vit-large-p16_8xb512-fp16-coslr-800e_in1k_20220825-df72726a.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 90
+ Batch Size: 16384
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 73.7
+ Config: configs/benchmarks/classification/imagenet/vit-large-p16_linear-8xb2048-coslr-90e_in1k.py
+ - Type: Image Classification
+ Metadata:
+ Epochs: 50
+ Batch Size: 1024
+ Results:
+ - Task: Fine-tuning
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 85.4
+ Config: configs/benchmarks/classification/imagenet/vit-large-p16_ft-8xb128-coslr-50e_in1k.py
+ - Name: mae_vit-large-p16_8xb512-amp-coslr-1600e_in1k
+ In Collection: MAE
+ Metadata:
+ Epochs: 1600
+ Batch Size: 4096
+ Results: null
+ Config: configs/selfsup/mae/mae_vit-large-p16_8xb512-amp-coslr-1600e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-large-p16_8xb512-fp16-coslr-1600e_in1k/mae_vit-large-p16_8xb512-fp16-coslr-1600e_in1k_20220825-cc7e98c9.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 90
+ Batch Size: 16384
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 75.5
+ Config: configs/benchmarks/classification/imagenet/vit-large-p16_linear-8xb2048-coslr-90e_in1k.py
+ - Type: Image Classification
+ Metadata:
+ Epochs: 50
+ Batch Size: 1024
+ Results:
+ - Task: Fine-tuning
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 85.7
+ Config: configs/benchmarks/classification/imagenet/vit-large-p16_ft-8xb128-coslr-50e_in1k.py
+ - Name: mae_vit-huge-p16_8xb512-amp-coslr-1600e_in1k.py
+ In Collection: MAE
+ Metadata:
+ Epochs: 1600
+ Batch Size: 4096
+ Results: null
+ Config: configs/selfsup/mae/mae_vit-huge-p16_8xb512-amp-coslr-1600e_in1k.py.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k_20220916-ff848775.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 50
+ Batch Size: 1024
+ Results:
+ - Task: Fine-tuning
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 86.9
+ Config: configs/benchmarks/classification/imagenet/vit-large-p16_ft-8xb128-coslr-50e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k/vit-huge-p16_ft-8xb128-coslr-50e_in1k/vit-huge-p16_ft-8xb128-coslr-50e_in1k_20220916-0bfc9bfd.pth
+ - Type: Image Classification
+ Metadata:
+ Epochs: 50
+ Batch Size: 256
+ Results:
+ - Task: Fine-tuning
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 87.3
+ Config: configs/benchmarks/classification/imagenet/vit-huge-p16_ft-32xb8-coslr-50e_in1k-448.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k/vit-huge-p16_ft-32xb8-coslr-50e_in1k-448/vit-huge-p16_ft-32xb8-coslr-50e_in1k-448_20220916-95b6a0ce.pth
diff --git a/configs/selfsup/mocov2/metafile.yml b/configs/selfsup/mocov2/metafile.yml
index ac8d71b2..a15b40ab 100644
--- a/configs/selfsup/mocov2/metafile.yml
+++ b/configs/selfsup/mocov2/metafile.yml
@@ -20,10 +20,18 @@ Models:
Metadata:
Epochs: 200
Batch Size: 256
- Results:
- - Task: Self-Supervised Image Classification
- Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 67.58
+ Results: null
Config: configs/selfsup/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k.py
- Weights: https://download.openmmlab.com/mmselfsup/moco/mocov2_resnet50_8xb32-coslr-200e_in1k_20220225-89e03af4.pth
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k/mocov2_resnet50_8xb32-coslr-200e_in1k_20220825-b6d23c86.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 100
+ Batch Size: 256
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 67.5
+ Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k/resnet50_linear-8xb32-steplr-100e_in1k/resnet50_linear-8xb32-steplr-100e_in1k_20220825-994c4128.pth
diff --git a/configs/selfsup/npid/metafile.yml b/configs/selfsup/npid/metafile.yml
index bdb2235d..9e376b60 100644
--- a/configs/selfsup/npid/metafile.yml
+++ b/configs/selfsup/npid/metafile.yml
@@ -20,10 +20,18 @@ Models:
Metadata:
Epochs: 200
Batch Size: 256
- Results:
- - Task: Self-Supervised Image Classification
- Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 57.97
+ Results: null
Config: configs/selfsup/npid/npid_resnet50_8xb32-steplr-200e_in1k.py
- Weights: https://download.openmmlab.com/mmselfsup/npid/npid_resnet50_8xb32-steplr-200e_in1k_20220225-5fbbda2a.pth
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/npid/npid_resnet50_8xb32-steplr-200e_in1k/npid_resnet50_8xb32-steplr-200e_in1k_20220825-a67c5440.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 100
+ Batch Size: 256
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 58.3
+ Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/npid/npid_resnet50_8xb32-steplr-200e_in1k/resnet50_linear-8xb32-steplr-100e_in1k/resnet50_linear-8xb32-steplr-100e_in1k_20220825-661b736e.pth
diff --git a/configs/selfsup/relative_loc/metafile.yml b/configs/selfsup/relative_loc/metafile.yml
index ebb6494c..f03d139c 100644
--- a/configs/selfsup/relative_loc/metafile.yml
+++ b/configs/selfsup/relative_loc/metafile.yml
@@ -19,10 +19,18 @@ Models:
Metadata:
Epochs: 70
Batch Size: 512
- Results:
- - Task: Self-Supervised Image Classification
- Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 38.78
+ Results: null
Config: configs/selfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k.py
- Weights: https://download.openmmlab.com/mmselfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k_20220225-84784688.pth
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k/relative-loc_resnet50_8xb64-steplr-70e_in1k_20220825-daae1b41.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 100
+ Batch Size: 256
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 40.4
+ Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k/resnet50_linear-8xb32-steplr-100e_in1k/resnet50_linear-8xb32-steplr-100e_in1k_20220825-c2a0b188.pth
diff --git a/configs/selfsup/rotation_pred/metafile.yml b/configs/selfsup/rotation_pred/metafile.yml
index c27568aa..58a46311 100644
--- a/configs/selfsup/rotation_pred/metafile.yml
+++ b/configs/selfsup/rotation_pred/metafile.yml
@@ -19,10 +19,18 @@ Models:
Metadata:
Epochs: 70
Batch Size: 128
- Results:
- - Task: Self-Supervised Image Classification
- Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 48.12
+ Results: null
Config: configs/selfsup/rotation_pred/rotation-pred_resnet50_8xb16-steplr-70e_in1k.py
- Weights: https://download.openmmlab.com/mmselfsup/rotation_pred/rotation-pred_resnet50_8xb16-steplr-70e_in1k_20220225-5b9f06a0.pth
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/rotation_pred/rotation-pred_resnet50_8xb16-steplr-70e_in1k/rotation-pred_resnet50_8xb16-steplr-70e_in1k_20220825-a8bf5f69.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 100
+ Batch Size: 256
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 47.0
+ Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/rotation_pred/rotation-pred_resnet50_8xb16-steplr-70e_in1k/resnet50_linear-8xb32-steplr-100e_in1k/resnet50_linear-8xb32-steplr-100e_in1k_20220825-7c6edcb3.pth
diff --git a/configs/selfsup/simclr/metafile.yml b/configs/selfsup/simclr/metafile.yml
index e1339713..5840ebf7 100644
--- a/configs/selfsup/simclr/metafile.yml
+++ b/configs/selfsup/simclr/metafile.yml
@@ -19,22 +19,58 @@ Models:
Metadata:
Epochs: 200
Batch Size: 256
- Results:
- - Task: Self-Supervised Image Classification
- Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 62.56
+ Results: null
Config: configs/selfsup/simclr/simclr_resnet50_8xb32-coslr-200e_in1k.py
- Weights: https://download.openmmlab.com/mmselfsup/simclr/simclr_resnet50_8xb32-coslr-200e_in1k_20220428-46ef6bb9.pth
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simclr/simclr_resnet50_8xb32-coslr-200e_in1k/simclr_resnet50_8xb32-coslr-200e_in1k_20220825-15f807a4.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 90
+ Batch Size: 4096
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 62.7
+ Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb512-coslr-90e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simclr/simclr_resnet50_16xb256-coslr-200e_in1k/resnet50_linear-8xb512-coslr-90e_in1k/resnet50_linear-8xb512-coslr-90e_in1k_20220825-f12c0457.pth
- Name: simclr_resnet50_16xb256-coslr-200e_in1k
In Collection: SimCLR
Metadata:
Epochs: 200
Batch Size: 4096
- Results:
- - Task: Self-Supervised Image Classification
- Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 66.66
+ Results: null
Config: configs/selfsup/simclr/simclr_resnet50_16xb256-coslr-200e_in1k.py
- Weights: https://download.openmmlab.com/mmselfsup/simclr/simclr_resnet50_16xb256-coslr-200e_in1k_20220428-8c24b063.pth
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simclr/simclr_resnet50_16xb256-coslr-200e_in1k/simclr_resnet50_16xb256-coslr-200e_in1k_20220825-4d9cce50.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 90
+ Batch Size: 4096
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 66.9
+ Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb512-coslr-90e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simclr/simclr_resnet50_16xb256-coslr-200e_in1k/resnet50_linear-8xb512-coslr-90e_in1k/resnet50_linear-8xb512-coslr-90e_in1k_20220825-f12c0457.pth
+ - Name: simclr_resnet50_16xb256-coslr-800e_in1k
+ In Collection: SimCLR
+ Metadata:
+ Epochs: 200
+ Batch Size: 4096
+ Results: null
+ Config: configs/selfsup/simclr/simclr_resnet50_16xb256-coslr-800e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simclr/simclr_resnet50_16xb256-coslr-800e_in1k/simclr_resnet50_16xb256-coslr-800e_in1k_20220825-85fcc4de.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 90
+ Batch Size: 4096
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 69.2
+ Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb512-coslr-90e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simclr/simclr_resnet50_16xb256-coslr-800e_in1k/resnet50_linear-8xb512-coslr-90e_in1k/resnet50_linear-8xb512-coslr-90e_in1k_20220825-b80ae1e5.pth
diff --git a/configs/selfsup/simmim/metafile.yml b/configs/selfsup/simmim/metafile.yml
index 881494bf..df07a805 100644
--- a/configs/selfsup/simmim/metafile.yml
+++ b/configs/selfsup/simmim/metafile.yml
@@ -7,22 +7,79 @@ Collections:
Training Resources: 16x A100 GPUs
Architecture:
- Swin
- - ViT
Paper:
URL: https://arxiv.org/abs/2111.09886
Title: "SimMIM: A Simple Framework for Masked Image Modeling"
README: configs/selfsup/simmim/README.md
Models:
- - Name: simmim_swin-base_8xb256-coslr-100e_in1k-192
+ - Name: simmim_swin-base_16xb128-coslr-100e_in1k-192
In Collection: SimMIM
Metadata:
Epochs: 100
Batch Size: 2048
- Results:
- - Task: Self-Supervised Image Classification
- Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 82.9
+ Results: null
Config: configs/selfsup/simmim/simmim_swin-base_16xb128-coslr-100e_in1k-192.py
- Weights: https://download.openmmlab.com/mmselfsup/simmim/simmim_swin-base_16xb128-coslr-100e_in1k-192_20220316-1d090125.pth
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simmim/simmim_swin-base_8xb256-amp-coslr-100e_in1k-192/simmim_swin-base_8xb256-amp-coslr-100e_in1k-192_20220829-0e15782d.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 100
+ Batch Size: 2048
+ Results:
+ - Task: Fine-tuning
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 82.7
+ Config: configs/benchmarks/classification/imagenet/swin-base_ft-8xb256-coslr-100e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simmim/simmim_swin-base_8xb256-amp-coslr-100e_in1k-192/swin-base_ft-8xb256-coslr-100e_in1k/swin-base_ft-8xb256-coslr-100e_in1k_20220829-9cf23aa1.pth
+ - Type: Image Classification
+ Metadata:
+ Epochs: 100
+ Batch Size: 2048
+ Results:
+ - Task: Fine-tuning
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 83.5
+ Config: configs/benchmarks/classification/imagenet/swin-base_ft-8xb256-coslr-100e_in1k-224.py
+ - Name: simmim_swin-base_16xb128-coslr-800e_in1k-192
+ In Collection: SimMIM
+ Metadata:
+ Epochs: 100
+ Batch Size: 2048
+ Results: null
+ Config: configs/selfsup/simmim/simmim_swin-base_16xb128-coslr-800e_in1k-192.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simmim/simmim_swin-base_16xb128-amp-coslr-800e_in1k-192/simmim_swin-base_16xb128-amp-coslr-800e_in1k-192_20220916-a0e931ac.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 100
+ Batch Size: 2048
+ Results:
+ - Task: Fine-tuning
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 83.8
+ Config: configs/benchmarks/classification/imagenet/swin-base_ft-8xb256-coslr-100e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simmim/simmim_swin-base_8xb256-amp-coslr-100e_in1k-192/swin-base_ft-8xb256-coslr-100e_in1k/swin-base_ft-8xb256-coslr-100e_in1k_20220829-9cf23aa1.pth
+ - Name: simmim_swin-large_16xb128-coslr-800e_in1k-192
+ In Collection: SimMIM
+ Metadata:
+ Epochs: 100
+ Batch Size: 2048
+ Results: null
+ Config: configs/selfsup/simmim/simmim_swin-base_16xb128-coslr-800e_in1k-192.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simmim/simmim_swin-large_16xb128-amp-coslr-800e_in1k-192/simmim_swin-large_16xb128-amp-coslr-800e_in1k-192_20220916-4ad216d3.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 100
+ Batch Size: 2048
+ Results:
+ - Task: Fine-tuning
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 84.8
+ Config: configs/benchmarks/classification/imagenet/swin-large_ft-8xb256-coslr-ws14-100e_in1k-224.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simmim/simmim_swin-large_16xb128-amp-coslr-800e_in1k-192/swin-large_ft-8xb256-coslr-ws14-100e_in1k-224/swin-large_ft-8xb256-coslr-ws14-100e_in1k-224_20220916-d4865790.pth
diff --git a/configs/selfsup/simsiam/metafile.yml b/configs/selfsup/simsiam/metafile.yml
index 9c46fd38..aec49c5a 100644
--- a/configs/selfsup/simsiam/metafile.yml
+++ b/configs/selfsup/simsiam/metafile.yml
@@ -19,22 +19,38 @@ Models:
Metadata:
Epochs: 100
Batch Size: 256
- Results:
- - Task: Self-Supervised Image Classification
- Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 68.28
+ Results: null
Config: configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k.py
- Weights: https://download.openmmlab.com/mmselfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k_20220225-68a88ad8.pth
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k/simsiam_resnet50_8xb32-coslr-100e_in1k_20220825-d07cb2e6.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 90
+ Batch Size: 4096
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 68.3
+ Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb512-coslr-90e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k/resnet50_linear-8xb512-coslr-90e_in1k/resnet50_linear-8xb512-coslr-90e_in1k_20220825-f53ba400.pth
- Name: simsiam_resnet50_8xb32-coslr-200e_in1k
In Collection: SimSiam
Metadata:
Epochs: 200
Batch Size: 256
- Results:
- - Task: Self-Supervised Image Classification
- Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 69.84
+ Results: null
Config: configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-200e_in1k.py
- Weights: https://download.openmmlab.com/mmselfsup/simsiam/simsiam_resnet50_8xb32-coslr-200e_in1k_20220225-2f488143.pth
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simsiam/simsiam_resnet50_8xb32-coslr-200e_in1k/simsiam_resnet50_8xb32-coslr-200e_in1k_20220825-efe91299.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 90
+ Batch Size: 4096
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 69.8
+ Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb512-coslr-90e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/simsiam/simsiam_resnet50_8xb32-coslr-200e_in1k/resnet50_linear-8xb512-coslr-90e_in1k/resnet50_linear-8xb512-coslr-90e_in1k_20220825-519b5135.pth
diff --git a/configs/selfsup/swav/metafile.yml b/configs/selfsup/swav/metafile.yml
index 688bd618..96f8b316 100644
--- a/configs/selfsup/swav/metafile.yml
+++ b/configs/selfsup/swav/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: 70.47
+ Results: null
Config: configs/selfsup/swav/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96.py
- Weights: https://download.openmmlab.com/mmselfsup/swav/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96_20220225-0497dd5d.pth
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/swav/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96_20220825-5b3fc7fc.pth
+ Downstream:
+ - Type: Image Classification
+ Metadata:
+ Epochs: 100
+ Batch Size: 256
+ Results:
+ - Task: Linear Evaluation
+ Dataset: ImageNet-1k
+ Metrics:
+ Top 1 Accuracy: 70.5
+ Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-coslr-100e_in1k.py
+ Weights: https://download.openmmlab.com/mmselfsup/1.x/swav/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96/resnet50_linear-8xb32-coslr-100e_in1k/resnet50_linear-8xb32-coslr-100e_in1k_20220825-80341e08.pth
diff --git a/docs/en/model_zoo.md b/docs/en/model_zoo.md
index 3a91816a..8da404c2 100644
--- a/docs/en/model_zoo.md
+++ b/docs/en/model_zoo.md
@@ -107,7 +107,7 @@ ImageNet has multiple versions, but the most commonly used one is ILSVRC 2012. T
| ResNet50 |
200 |
256 |
- 62.7 |
+ 67.5 |
/ |
config | model | log |
config | model | log |
diff --git a/model-index.yml b/model-index.yml
index 4993c013..5e44c744 100644
--- a/model-index.yml
+++ b/model-index.yml
@@ -12,6 +12,7 @@ Import:
- configs/selfsup/simclr/metafile.yml
- configs/selfsup/simsiam/metafile.yml
- configs/selfsup/swav/metafile.yml
- - configs/selfsup/mae/metafile.yaml
+ - configs/selfsup/mae/metafile.yml
- configs/selfsup/simmim/metafile.yml
- configs/selfsup/barlowtwins/metafile.yml
+ - configs/selfsup/cae/metafile.yml