Rethinking pose estimation in crowds: overcoming the detection information bottleneck and ambiguity
Here we provide neural networks weights for the best models in our article "Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity", ICCV 2023. Each model has the naming convention "dataset"-"modeltype".pth. These pth files can be loaded with PyTorch. The code to load and use the models is available at: https://github.com/amathislab/BUCTD [Note: The weights for OCHuman, are called COCO-* as one only trains on COCO. So OCHuman-X := COCO-X]. We also share the predictions from various bottom-up models to reproduce the training stored in *.json format (compressed as zip files). See our repository for more details.
2023
v4
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