/BLPose

PyTorch's Pose Estimation Toolbox

Primary LanguagePythonMIT LicenseMIT

BLPose (BaseLine Pose Estimation)

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PyTorch's Pose Estimation Toolbox

Requirement

  • Python 3
  • PyTorch >= 1.0.0

Supported Module

  • Backbone
    • VGG
      • VGG11
      • VGG13
      • VGG16
      • VGG19
    • ResNet
      • ResNet18
      • ResNet34
      • ResNet50
      • ResNet101
      • ResNet152
    • SE ResNet
      • SE ResNet50
      • SE ResNet101
      • SE ResNet152
    • MobileNet v1 (1.0)
    • MobileNet v2 (1.0)
  • Model
    • OpenPose
  • Metric
    • Average Meter
  • Others
    • Xavier/MSRA initialization (support zero gamma in last BatchNorm)
    • Mixed precision training
    • Online Hard Example Mining
    • Precise BatchNorm (comming soon...)

| Backbone \ Model |

Changelog

See Changelog

References

  • Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
  • Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861 (2017).
  • Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
  • He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
  • Hu, Jie, Li Shen, and Gang Sun. "Squeeze-and-excitation networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.