Zhou, MuStoffl, LucasMathis, MackenzieMathis, Alexander2023-10-252023-10-252023-10-25202310.5281/zenodo.10039883https://infoscience.epfl.ch/handle/20.500.14299/201910Here 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.enCOCOCrowdPoseOCHumanPose estimationICCV 2023dataset486d2de4-baf4-485b-bfb0-f8fd69d1f8c5