mmpretrain/configs/swin_transformer/metafile.yml

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YAML

Collections:
- Name: Swin-Transformer
Metadata:
Training Data: ImageNet
Training Techniques:
- AdamW
- Weight Decay
Training Resources: 16x V100 GPUs
Epochs: 300
Batch Size: 1024
Architecture:
- Shift Window Multihead Self Attention
Paper: https://arxiv.org/pdf/2103.14030.pdf
README: configs/swin_transformer/README.md
Models:
- Config: configs/swin_transformer/swin_tiny_224_b16x64_300e_imagenet.py
In Collection: Swin-Transformer
Metadata:
FLOPs: 4360000000
Parameters: 28290000
Training Data: ImageNet
Training Resources: 16x 1080 GPUs
Epochs: 300
Batch Size: 1024
Name: swin_tiny_224_imagenet
Results:
- Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.18
Top 5 Accuracy: 95.61
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth
- Config: configs/swin_transformer/swin_small_224_b16x64_300e_imagenet.py
In Collection: Swin-Transformer
Metadata:
FLOPs: 8520000000
Parameters: 48610000
Training Data: ImageNet
Training Resources: 16x 1080 GPUs
Epochs: 300
Batch Size: 1024
Name: swin_small_224_imagenet
Results:
- Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.02
Top 5 Accuracy: 96.29
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219-7f9d988b.pth
- Config: configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py
In Collection: Swin-Transformer
Metadata:
FLOPs: 15140000000
Parameters: 87770000
Training Data: ImageNet
Training Resources: 16x 1080 GPUs
Epochs: 300
Batch Size: 1024
Name: swin_base_224_imagenet
Results:
- Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.36
Top 5 Accuracy: 96.44
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742-93230b0d.pth