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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>
39 lines
1.4 KiB
YAML
39 lines
1.4 KiB
YAML
Collections:
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- Name: DenseCL
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Metadata:
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Training Data: ImageNet-1k
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x V100 GPUs
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Architecture:
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- ResNet
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Paper:
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URL: https://arxiv.org/abs/2011.09157
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Title: "Dense contrastive learning for self-supervised visual pre-training"
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README: configs/densecl/README.md
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Models:
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- Name: densecl_resnet50_8xb32-coslr-200e_in1k
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In Collection: DenseCL
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Metadata:
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Epochs: 200
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Batch Size: 256
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Results: null
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Config: configs/densecl/densecl_resnet50_8xb32-coslr-200e_in1k.py
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Weights: https://download.openmmlab.com/mmselfsup/1.x/densecl/densecl_resnet50_8xb32-coslr-200e_in1k/densecl_resnet50_8xb32-coslr-200e_in1k_20220825-3078723b.pth
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Downstream:
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- resnet50_densecl-pre_8xb32-linear-steplr-100e_in1k
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- Name: resnet50_densecl-pre_8xb32-linear-steplr-100e_in1k
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In Collection: DenseCL
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Metadata:
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Epochs: 100
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Batch Size: 256
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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: 63.5
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Config: configs/densecl/benchmarks/resnet50_8xb32-linear-steplr-100e_in1k.py
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Weights: https://download.openmmlab.com/mmselfsup/1.x/densecl/densecl_resnet50_8xb32-coslr-200e_in1k/resnet50_linear-8xb32-steplr-100e_in1k/resnet50_linear-8xb32-steplr-100e_in1k_20220825-f0f0a579.pth
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