42 lines
1.3 KiB
YAML
42 lines
1.3 KiB
YAML
Collections:
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- Name: BEiTv2
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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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- Dropout
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- GELU
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- Layer Normalization
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- Multi-Head Attention
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- Scaled Dot-Product Attention
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- Tanh Activation
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Paper:
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URL: https://arxiv.org/abs/2208.06366
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Title: 'BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers'
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README: configs/beitv2/README.md
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Code:
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URL: https://github.com/open-mmlab/mmclassification/blob/dev-1.x/mmcls/models/backbones/beit.py
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Version: v1.0.0rc4
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Models:
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- Name: beitv2-base_3rdparty_in1k
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In Collection: BEiTv2
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Metadata:
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FLOPs: 17581219584
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Parameters: 86530984
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Training Data:
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- ImageNet-21k
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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: 86.47
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Top 5 Accuracy: 97.99
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Weights: https://download.openmmlab.com/mmclassification/v0/beit/beitv2-base_3rdparty_in1k_20221114-73e11905.pth
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Converted From:
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Weights: https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft21kto1k.pth
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Code: https://github.com/microsoft/unilm/tree/master/beit2
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Config: configs/beitv2/beitv2-base-p16_8xb64_in1k.py
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