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