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tracking-evaluation【数据与研究】Configure and run PyTracking trackers on datasets, videos, webcams, and experiment functions.
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Use this sub-skill when a task is about planning or running PyTracking evaluation/runtime commands:
trackerlist(...) and get_dataset(...)Do not use this sub-skill for:
analysis-and-packagingltr-trainingtracker-developmentlocal.py paths, network/checkpoint path, and expected result directory.pytracking and ltr; full network-backed tracker runs usually require a CUDA-capable PyTorch stack and pretrained checkpoints.pytracking/evaluation/local.py must exist and contain the dataset, network, and result paths needed by the chosen alias. Some VOS, AVisT, and LaGOT aliases require extra local fields; see datasets and results.tracker_name and tracker_param are import names, not display labels. Tracker imports pytracking.tracker.<tracker_name> and pytracking.parameter.<tracker_name>.<tracker_param>.From this sub-skill directory, generate but do not run a dataset command:
python scripts/build_tracking_command.py dataset --tracker dimp --param dimp50 --dataset otb --sequence Soccer --debug 0
List accepted dataset aliases:
python scripts/build_tracking_command.py --list-datasets
Generate a video command with a numeric initialization box:
python scripts/build_tracking_command.py video --tracker atom --param default --videofile /path/to/video.mp4 --optional-box 100 80 60 40