---

name: pytracking description: "Use PyTracking and LTR for visual object tracking, video object segmentation, tracker evaluation, result analysis, custom tracker development, and PyTorch training workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

PyTracking Repo Skill

Use this skill when a task involves PyTracking, LTR, or visual object tracking/video object segmentation workflows from the PyTracking repository: running pretrained trackers, configuring datasets and checkpoints, analyzing benchmark results, packaging submissions, implementing custom trackers, or training LTR models.

First checks

1. Read repository provenance when deciding whether this skill matches a target checkout or whether refresh-repo-skill is needed.

2. Read configuration and environment before running anything that needs datasets, checkpoints, CUDA, Visdom, VOT, or training workspaces.

3. Use the read-only setup checker before execution:

   python scripts/check_pytracking_setup.py --repo-root /path/to/pytracking --require-dataset otb
   

4. For tracker/model name selection, read tracker and model catalog.

5. For install/import/backend/data/checkpoint failures that do not belong to one workflow, read cross-cutting troubleshooting.

Route by task

Task shape	Read next
Run a tracker on a dataset, one sequence, a video, webcam, or an experiment function; choose dataset aliases; reason about run_tracker, run_video, run_webcam, run_experiment, Tracker, trackerlist, debug, Visdom, and result directories.	tracking-evaluation
Analyze saved bounding-box/VOS results, plan plots, replay outputs, package GOT-10k or TrackingNet submissions, handle raw result archives, or plan VOT toolkit integration.	analysis-and-packaging
Configure, launch, inspect, or modify LTR training settings, datasets, samplers, actors, trainers, models, checkpoints, TensorBoard, and CUDA training behavior.	ltr-training
Implement, adapt, register, or debug a custom tracker or parameter file; inspect BaseTracker, TrackerParams, output dictionaries, multi-object/segmentation conventions, features, and tracking libs.	tracker-development

Operating boundaries

• This skill teaches PyTracking operation; it does not contain pretrained model weights, datasets, raw benchmark results, or VOT workspaces.
• Full tracker and training workflows usually need a CUDA-capable PyTorch environment plus external datasets/checkpoints. CPU import checks are useful for command/config validation but are not proof of full tracker performance.
• The upstream shell installer is broad and side-effecting. Do not run it automatically; translate the selected workflow into minimal environment, dependency, data, and checkpoint checks.
• Do not launch full benchmark evaluations, training epochs, webcam/video GUI sessions, Visdom servers, VOT toolkits, Google Drive downloads, or archive packaging without explicit user approval for side effects, runtime, data, and hardware.
• PyTracking is source-tree-style in this snapshot. Commands commonly run from a target checkout root or with that checkout root on PYTHONPATH; avoid assuming a normal pip install . package.

Minimal import and CUDA smoke

Use this in the user's target environment before claiming runtime readiness:

python - <<'PY'
import torch
import pytracking, ltr
print('pytracking', pytracking.__file__)
print('ltr', ltr.__file__)
print('torch', torch.__version__, 'cuda_available', torch.cuda.is_available())
if torch.cuda.is_available():
    print(torch.cuda.get_device_name(0), torch.cuda.get_device_capability(0))
    torch.empty((1,), device='cuda')
PY

If CUDA is unavailable, narrow the task to source/config/command planning or use only explicitly CPU-capable mechanics; do not report network-backed tracker/training execution as verified.

Common commands to plan, not blindly run

Build a dataset command without executing it:

python sub-skills/tracking-evaluation/scripts/build_tracking_command.py dataset --tracker dimp --param dimp50 --dataset otb --sequence Soccer --debug 0 --explain

Build an LTR training command without launching training:

python sub-skills/ltr-training/scripts/build_training_command.py --repo-root /path/to/pytracking bbreg atom --explain

Plan a benchmark packaging layout without writing a zip:

python sub-skills/analysis-and-packaging/scripts/plan_result_packaging.py got10k --tracker-name dimp --parameter-name dimp50 --run-ids 0,1,2

Validate a custom tracker layout statically:

python sub-skills/tracker-development/scripts/validate_tracker_layout.py --repo-root /path/to/pytracking --tracker-name mytracker --param-name default

Verification stance

Generated guidance is self-contained in this skill tree. Source repository files were used as evidence, but runtime Markdown links and bundled scripts point inside this skill. When a future task needs real PyTracking execution, validate the target checkout, environment, datasets, checkpoints, and side effects first, then route to the focused sub-skill.