BoxMOT

BoxMOT is the skill to use when the user wants to track objects, run MOT benchmarks, train or compare ReID models, export ReID checkpoints, or switch trackers to native C++ backends.

Quick install and smoke check

For a public install, use the package itself:

pip install boxmot
boxmot --help
python -c "import boxmot; print(boxmot.__version__)"

If you are working from a source checkout, uv sync --all-extras --all-groups is the broadest dev-friendly install, but the runtime skill should stay usable from the published package too.

If the CLI or import fails, read:

• install and inspect
• cross-cutting troubleshooting
• repo provenance

Route map

tracking-and-trackers

Use this route for direct tracking, tracker construction, tracker defaults, AABB/OBB detection layouts, output schemas, and tracker-level debugging.

Typical prompts:

• track a webcam or video with a specific tracker
• explain tracker output columns or OBB input shapes
• instantiate a tracker from Python and inspect its results
• debug shape errors or missing det_ind values

Read:

• sub-skills/tracking-and-trackers/SKILL.md
• sub-skills/tracking-and-trackers/references/workflows.md
• sub-skills/tracking-and-trackers/references/data-formats.md

benchmark-workflows

Use this route for cached generate / eval / tune / research workflows, benchmark YAMLs, cache reuse, public detections, and postprocessing.

Typical prompts:

• generate caches for a benchmark
• reuse an existing cache with a different tracker
• tune tracker hyperparameters on MOT17 or MMOT
• debug benchmark config selection or split handling

Read:

• sub-skills/benchmark-workflows/SKILL.md
• sub-skills/benchmark-workflows/references/benchmark-workflows.md
• sub-skills/benchmark-workflows/references/configuration.md

reid-lifecycle

Use this route for training ReID backbones, evaluating checkpoints, comparing multiple checkpoints, exporting to ONNX/OpenVINO/TensorRT/TFLite, and generating embeddings.

Typical prompts:

• train a ReID model on a dataset or recipe
• evaluate or compare checkpoints across target datasets
• export a ReID model for deployment
• debug dataset, preprocess, or export-format issues

Read:

• sub-skills/reid-lifecycle/SKILL.md
• sub-skills/reid-lifecycle/references/reid-lifecycle.md
• sub-skills/reid-lifecycle/references/model-and-export-overview.md

native-cpp-backends

Use this route for --tracker-backend cpp, boxmot build, native C++ live tracking, native replay, and embedding the trackers in your own C++ program.

Typical prompts:

• build the native trackers
• switch a supported tracker to the C++ backend
• understand supported native tracker coverage
• troubleshoot build-tool or backend-selection failures

Read:

• sub-skills/native-cpp-backends/SKILL.md
• sub-skills/native-cpp-backends/references/native-cpp.md
• sub-skills/native-cpp-backends/references/troubleshooting.md

Public package facts

• CLI entry point: boxmot
• Public Python facade: boxmot.BoxMOT
• Public detector wrapper: boxmot.Detector
• Public ReID wrapper: boxmot.ReIDModel
• Package-level tracker export: boxmot.trackers.OccluBoost
• Supported tracker names are defined in boxmot.trackers.registry.TRACKER_MAPPING
• Detection layouts switch automatically from tensor shape:
  • AABB: (N, 6) input -> (N, 8) output
  • OBB: (N, 7) input -> (N, 9) output

When to start here

If the user only says "use BoxMOT", start with the route that matches the task family and then open the nearest sub-skill reference. If the request names a CLI flag, benchmark id, tracker name, ReID checkpoint, output schema, or C++ backend, use that signal to choose the route immediately.