OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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33 lines
1.0 KiB
33 lines
1.0 KiB
Collections: |
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- Name: AutoAssign |
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Metadata: |
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Training Data: COCO |
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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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- AutoAssign |
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- FPN |
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- ResNet |
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Paper: |
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URL: https://arxiv.org/abs/2007.03496 |
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Title: 'AutoAssign: Differentiable Label Assignment for Dense Object Detection' |
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README: configs/autoassign/README.md |
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Code: |
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URL: https://github.com/open-mmlab/mmdetection/blob/v2.12.0/mmdet/models/detectors/autoassign.py#L6 |
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Version: v2.12.0 |
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Models: |
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- Name: autoassign_r50_fpn_8x2_1x_coco |
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In Collection: AutoAssign |
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Config: configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py |
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Metadata: |
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Training Memory (GB): 4.08 |
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Epochs: 12 |
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Results: |
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- Task: Object Detection |
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Dataset: COCO |
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Metrics: |
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box AP: 40.4 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth
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