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Collections: |
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- Name: Rethinking Classification and Localization for Object Detection |
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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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- FPN |
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- RPN |
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- ResNet |
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- RoIAlign |
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Paper: |
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URL: https://arxiv.org/pdf/1904.06493 |
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Title: 'Rethinking Classification and Localization for Object Detection' |
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README: configs/double_heads/README.md |
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Code: |
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URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/roi_heads/double_roi_head.py#L6 |
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Version: v2.0.0 |
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Models: |
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- Name: dh_faster_rcnn_r50_fpn_1x_coco |
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In Collection: Rethinking Classification and Localization for Object Detection |
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Config: configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py |
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Metadata: |
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Training Memory (GB): 6.8 |
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inference time (ms/im): |
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- value: 105.26 |
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hardware: V100 |
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backend: PyTorch |
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batch size: 1 |
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mode: FP32 |
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resolution: (800, 1333) |
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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: 40.0 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth |
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