mirror of
https://github.com/open-mmlab/mmpretrain.git
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89000c10eb
* update base datasets * update base * update barlowtwins * update with new convention * update * update * update * add schedule * add densecl * add eva * add mae * add maskfeat * add milan and mixmim * add moco * add swav simclr * add simmim and simsiam * refine * update * add to model index * update config inheritance * fix error in metafile * Update pre-commit and metafile check script * update metafile * fix name error * Fix classification model name and config name --------- Co-authored-by: mzr1996 <mzr1996@163.com>
63 lines
2.2 KiB
YAML
63 lines
2.2 KiB
YAML
Collections:
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- Name: MixMIM
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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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Title: 'MixMIM: Mixed and Masked Image Modeling for Efficient Visual Representation Learning'
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URL: https://arxiv.org/abs/2205.13137
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README: configs/mixmim/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/mixmim.py
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Version: v1.0.0rc4
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Models:
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- Name: mixmim-base_3rdparty_in1k
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Metadata:
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FLOPs: 16352000000
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Parameters: 88344000
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Training Data:
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- ImageNet-1k
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In Collection: MixMIM
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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: 84.6
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Top 5 Accuracy: 97.0
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Weights: https://download.openmmlab.com/mmclassification/v0/mixmim/mixmim-base_3rdparty_in1k_20221206-e40e2c8c.pth
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Config: configs/mixmim/benchmarks/mixmim-base_8xb64_in1k.py
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Converted From:
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Code: https://github.com/Sense-X/MixMIM
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- Name: mixmim_mixmim-base_16xb128-coslr-300e_in1k
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In Collection: MixMIM
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Metadata:
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Epochs: 300
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Batch Size: 2048
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Results: null
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Config: configs/mixmim/mixmim_mixmim-base_16xb128-coslr-300e_in1k.py
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Weights: https://download.openmmlab.com/mmselfsup/1.x/mixmim/mixmim-base-p16_16xb128-coslr-300e_in1k/mixmim-base-p16_16xb128-coslr-300e_in1k_20221208-44fe8d2c.pth
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Downstream:
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- mixmim-base_mixmim-pre_8xb128-coslr-100e_in1k
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- Name: mixmim-base_mixmim-pre_8xb128-coslr-100e_in1k
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In Collection: MILAN
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Metadata:
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Epochs: 100
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Batch Size: 1024
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Results:
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- Task: Image Classification
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Dataset: ImageNet-1k
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Metrics:
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Top 1 Accuracy: 84.63
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Config: configs/mixmim/benchmarks/mixmim-base_8xb128-coslr-100e_in1k.py
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Weights: https://download.openmmlab.com/mmselfsup/1.x/mixmim/mixmim-base-p16_16xb128-coslr-300e_in1k/mixmim-base-p16_ft-8xb128-coslr-100e_in1k/mixmim-base-p16_ft-8xb128-coslr-100e_in1k_20221208-41ecada9.pth
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