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Collections:
- Name: CentripetalNet
Metadata:
Training Data: COCO
Training Techniques:
- Adam
Training Resources: 16x V100 GPUs
Architecture:
- Corner Pooling
- Stacked Hourglass Network
Paper:
URL: https://arxiv.org/abs/2003.09119
Title: 'CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection'
README: configs/centripetalnet/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.5.0/mmdet/models/detectors/cornernet.py#L9
Version: v2.5.0
Models:
- Name: centripetalnet_hourglass104_mstest_16x6_210e_coco
In Collection: CentripetalNet
Config: configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py
Metadata:
Batch Size: 96
Training Memory (GB): 16.7
inference time (ms/im):
- value: 270.27
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 210
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.8
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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