96 lines
3.2 KiB
YAML
96 lines
3.2 KiB
YAML
Collections:
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- Name: MViT V2
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Metadata:
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Architecture:
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- Attention Dropout
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- Convolution
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- Dense Connections
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- GELU
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- Layer Normalization
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- Scaled Dot-Product Attention
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- Attention Pooling
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Paper:
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URL: http://openaccess.thecvf.com//content/CVPR2022/papers/Li_MViTv2_Improved_Multiscale_Vision_Transformers_for_Classification_and_Detection_CVPR_2022_paper.pdf
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Title: 'MViTv2: Improved Multiscale Vision Transformers for Classification and Detection'
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README: configs/mvit/README.md
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Code:
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URL: https://github.com/open-mmlab/mmpretrain/blob/v0.24.0/mmcls/models/backbones/mvit.py
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Version: v0.24.0
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Models:
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- Name: mvitv2-tiny_3rdparty_in1k
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In Collection: MViT V2
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Metadata:
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FLOPs: 4703510768
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Parameters: 24173320
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Training Data:
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- ImageNet-1k
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Results:
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- Dataset: ImageNet-1k
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Task: Image Classification
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Metrics:
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Top 1 Accuracy: 82.33
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Top 5 Accuracy: 96.15
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Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-tiny_3rdparty_in1k_20220722-db7beeef.pth
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Converted From:
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Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_T_in1k.pyth
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Code: https://github.com/facebookresearch/mvit
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Config: configs/mvit/mvitv2-tiny_8xb256_in1k.py
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- Name: mvitv2-small_3rdparty_in1k
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In Collection: MViT V2
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Metadata:
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FLOPs: 6997555136
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Parameters: 34870216
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Training Data:
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- ImageNet-1k
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Results:
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- Dataset: ImageNet-1k
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Task: Image Classification
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Metrics:
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Top 1 Accuracy: 83.63
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Top 5 Accuracy: 96.51
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Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-small_3rdparty_in1k_20220722-986bd741.pth
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Converted From:
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Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_S_in1k.pyth
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Code: https://github.com/facebookresearch/mvit
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Config: configs/mvit/mvitv2-small_8xb256_in1k.py
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- Name: mvitv2-base_3rdparty_in1k
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In Collection: MViT V2
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Metadata:
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FLOPs: 10157964400
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Parameters: 51472744
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Training Data:
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- ImageNet-1k
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Results:
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- Dataset: ImageNet-1k
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Task: Image Classification
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Metrics:
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Top 1 Accuracy: 84.34
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Top 5 Accuracy: 96.86
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Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-base_3rdparty_in1k_20220722-9c4f0a17.pth
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Converted From:
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Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_B_in1k.pyth
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Code: https://github.com/facebookresearch/mvit
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Config: configs/mvit/mvitv2-base_8xb256_in1k.py
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- Name: mvitv2-large_3rdparty_in1k
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In Collection: MViT V2
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Metadata:
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FLOPs: 43868151412
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Parameters: 217992952
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Training Data:
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- ImageNet-1k
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Results:
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- Dataset: ImageNet-1k
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Task: Image Classification
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Metrics:
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Top 1 Accuracy: 85.25
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Top 5 Accuracy: 97.14
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Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-large_3rdparty_in1k_20220722-2b57b983.pth
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Converted From:
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Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_L_in1k.pyth
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Code: https://github.com/facebookresearch/mvit
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Config: configs/mvit/mvitv2-large_8xb256_in1k.py
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