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HumanSegmentation

The project involves training a U2-Net Lite model for human segmentation using the P3M-10k dataset. Human segmentation refers to the process of accurately separating the human body from the background in an image or video.

Environment Setup

  • python >= 3.9 (Anaconda)
  • torch
  • torchvision
  • opencv
  • numpy
  • Pillow

Prepare Training Dataset

Used the P3M-10K dataset for training U2Net Lite model. The dataset contains ~10,000 images of people in various poses and environments. To augment the dataset, I applied horizontal flipping to the images, resulting in a total of ~20,000 training images. The dataset was split into training and validation sets with a ratio of 80:20.

Acknowledgements

  • U2net model is from original u2net repo. Thanks to Xuebin Qin for amazing repo.
  • Thanks to the creators of the P3M-10K dataset for providing a valuable resource.