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Configuration to run AlphaPose in Docker container
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Ruben van de Ven 5aa2d0fac1 Add script to convert alphapose-results to coco-in
2023-01-28 21:41:15 +01:00
detector Dockerfile for Alphapose 2023-01-19 10:49:43 +01:00
pretrained_models Dockerfile for Alphapose 2023-01-19 10:49:43 +01:00
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Dockerfile get up to date CUDA and Alphapose repo 2023-01-19 15:13:29 +01:00
downloadCOCO.sh download MSCoco for training AlphaPose 2023-01-19 11:55:24 +01:00
loop_alphapose_training.py Add script to convert alphapose-results to coco-in 2023-01-28 21:41:15 +01:00
README.md Dockerfile for Alphapose 2023-01-19 10:49:43 +01:00

README.md

AlphaPose Docker

Create a docker image for AlphaPose (code at GitHub).

Building the image

docker build --tag alphapose .

Usage

Before the repository can be used, it is necessary to download the required auxilary models, as per the AlphaPose installation guide.

  1. YOLOv3 Object detector can be place in the detector/yolo/data repository.
  2. (Optionally) YOLOX models go in the detector/yolox/data repository.
  3. A pretrained AlphaPose model can be place in the pretrained_models directory. See their Model Zoo for the various options.
  4. For pose tracking, see the pose tracking module. Make sure to add the necessary folder as a volume to the docker run command.

Running

docker run --rm --gpus all -v `pwd`/out:/out -v `pwd`/detector/yolox/data:/build/AlphaPose/detector/yolox/data -v `pwd`/detector/yolo/data:/build/AlphaPose/detector/yolo/data -v `pwd`/pretrained_models:/build/AlphaPose/pretrained_models alphapose python3 scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/fast_res50_256x192.pth --gpus 0 --indir examples/demo/ --save_img --vis_fast --outdir /out