---

name: ltr-training description: "Configure, launch, and modify PyTracking LTR training settings for ATOM, DiMP/PrDiMP, KeepTrack, KYS, LWL, RTS, TaMOs, and ToMP." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

LTR training operating skill

Use this sub-skill when the task is to configure, inspect, launch, resume, or modify the LTR training stack in a PyTracking checkout. It covers run_training, train_settings module/name pairs, local training configuration, datasets, data processing/sampling/loading, actors, trainers, models, checkpoints, TensorBoard, CUDA, and training-only optional dependencies.

Route elsewhere

• Runtime tracker execution, video/webcam/dataset evaluation, dataset alias selection, Visdom/debug behavior, and result directories belong to tracking-evaluation.
• Implementing a new runtime tracker class, parameter file, or tracker registry layout belongs to tracker-development.
• Result plotting, benchmark result packaging, VOT integration, and raw-result analysis belong to analysis-and-packaging.

Start here

1. Read references/workflows.md for setup, command construction, launch/resume, TensorBoard, and safe modification workflows.
2. Read references/training-settings.md to choose the valid train_module / train_name pair and identify datasets/checkpoints required by each family.
3. Read references/data-and-model-api.md before editing a setting file, adding a dataset, replacing a model, changing a sampler, or changing an actor/loss/trainer path.
4. Use scripts/build_training_command.py to validate available training settings and print a command. The script scans a target checkout and never launches training.
5. Use references/troubleshooting.md for local.py, dataset path, checkpoint, CUDA/OOM, TensorBoard, PreciseRoIPooling, and KYS correlation-sampler failures.

Quick command builder usage

From a PyTracking checkout:

python skills/disco/pytracking/sub-skills/ltr-training/scripts/build_training_command.py --list
python skills/disco/pytracking/sub-skills/ltr-training/scripts/build_training_command.py bbreg atom
python skills/disco/pytracking/sub-skills/ltr-training/scripts/build_training_command.py tomp tomp50 --no-cudnn-benchmark

The default output is a shell command in the source style, for example python ltr/run_training.py bbreg atom. With --no-cudnn-benchmark, the helper emits a Python API one-liner because the source CLI parses boolean strings unsafely; do not replace it with --cudnn_benchmark False unless you have fixed the parser.

Operating invariants

• Training is long-running and dataset/checkpoint dependent. Do not start full epochs unless the user explicitly requested execution and required data, workspace, checkpoints, CUDA/backend, and budget are available.
• train_module is the subdirectory under ltr/train_settings; train_name is the Python file stem in that subdirectory. Validate against the checkout instead of relying on prose examples.
• A writable local training configuration is mandatory. At minimum configure workspace_dir, tensorboard_dir, pretrained_networks, and every dataset path used by the selected setting.
• Checkpoints are saved under the configured workspace in a project path shaped like ltr/<train_module>/<train_name>; TensorBoard logs use the same project path under the configured TensorBoard directory.
• Most settings assume CUDA and large tracking datasets. Some settings set multi_gpu=True; some compute batch size from torch.cuda.device_count(). Adjust settings intentionally for CPU-only or single-GPU dry work.