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Collections: |
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- Name: CentripetalNet |
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Metadata: |
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Training Data: COCO |
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Training Techniques: |
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- Adam |
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Training Resources: 16x V100 GPUs |
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Architecture: |
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- Corner Pooling |
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- Stacked Hourglass Network |
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Paper: |
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URL: https://arxiv.org/abs/2003.09119 |
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Title: 'CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection' |
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README: configs/centripetalnet/README.md |
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Code: |
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URL: https://github.com/open-mmlab/mmdetection/blob/v2.5.0/mmdet/models/detectors/cornernet.py#L9 |
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Version: v2.5.0 |
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Models: |
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- Name: centripetalnet_hourglass104_mstest_16x6_210e_coco |
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In Collection: CentripetalNet |
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Config: configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py |
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Metadata: |
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Batch Size: 96 |
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Training Memory (GB): 16.7 |
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inference time (ms/im): |
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- value: 270.27 |
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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: 210 |
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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: 44.8 |
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Weights: https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth |
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