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ViTPose (simple version w/o mmcv)

An unofficial implementation of ViTPose [Y. Xu et al., 2022]
result_image

Usage

| Inference

python inference.py --image-path './examples/img1.jpg'

| Training

python train.py --config-path config.yaml --model-name 'b'
  • model_name must be in (b, l, h)

Note

  1. Download the trained model (.pth)
  2. Set the config. according to the trained model

Reference

All codes were written with reference to the official ViTPose repo.

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ViTPose Implementation for DL1_Project

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  • Jupyter Notebook 41.1%
  • Cuda 30.5%
  • Python 28.0%
  • Other 0.4%