Introduce a PyTorch inference path for SuperAnimal models, including backend selection in the UI, cached pose/detector runners, and JSON export of predictions. The legacy TensorFlow flow now maps model names separately from local directories, and MegaDetector device selection was corrected for yolov5 loading. The DeepLabCut dependency is also pinned to 3.0.2 to match the new backend integration.
Carry the filtered MegaDetector bounding boxes through cropping, visualization, and JSON export so pose results stay matched to the correct animal detections. This also removes the obsolete save_results module and hardens DLC inference by handling empty crop lists, converting crop channel order for DLCLive, and masking low-confidence keypoints with NaN.
Skip torch.hub GitHub API validation (fixes #10) (#12)
