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code_test/configs/centripetalnet/metafile.yml

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  1. Collections:

  2.   - Name: CentripetalNet

  3.     Metadata:

  4.       Training Data: COCO

  5.       Training Techniques:

  6.         - Adam

  7.       Training Resources: 16x V100 GPUs

  8.       Architecture:

  9.         - Corner Pooling

  10.         - Stacked Hourglass Network

  11.     Paper:

  12.       URL: https://arxiv.org/abs/2003.09119

  13.       Title: 'CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection'

  14.     README: configs/centripetalnet/README.md

  15.     Code:

  16.       URL: https://github.com/open-mmlab/mmdetection/blob/v2.5.0/mmdet/models/detectors/cornernet.py#L9

  17.       Version: v2.5.0

  18. 

  19. Models:

  20.   - Name: centripetalnet_hourglass104_mstest_16x6_210e_coco

  21.     In Collection: CentripetalNet

  22.     Config: configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py

  23.     Metadata:

  24.       Batch Size: 96

  25.       Training Memory (GB): 16.7

  26.       inference time (ms/im):

  27.         - value: 270.27

  28.           hardware: V100

  29.           backend: PyTorch

  30.           batch size: 1

  31.           mode: FP32

  32.           resolution: (800, 1333)

  33.       Epochs: 210

  34.     Results:

  35.       - Task: Object Detection

  36.         Dataset: COCO

  37.         Metrics:

  38.           box AP: 44.8

  39.     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