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name: paddle-detection description: "Guides PaddleDetection object detection, segmentation, keypoint, multi-object tracking, configuration, training, evaluation, inference, export, deployment, and PP-Human/PP-Vehicle pipeline workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PaddleDetection

Use this skill when a task names PaddleDetection, paddledet, PP-YOLO/PP-YOLOE/PicoDet, PaddleDetection YAML configs, tools/train.py, tools/infer.py, model export, PP-Human, PP-Vehicle, PP-Tracking, or PaddleDetection deployment.

This is an operating router, not a copy of the repository. Work against the user's PaddleDetection checkout or installed package, and use the bundled references/scripts in this skill for decisions and preflight checks. Do not assume the creation checkout or its private environment still exists.

First route

1. Identify the task family and choose exactly one primary route below.
2. Establish the target checkout/package version, PaddlePaddle device, dataset layout, config path, weights source, and whether network/GPU/vendor runtimes are allowed.
3. Run the bundled environment preflight before importing deep modules: scripts/check_paddledet_environment.py.
4. Keep use_gpu=false or --device=CPU for a deterministic smoke test unless CUDA/TensorRT is explicitly required and available.
5. Read references/troubleshooting.md whenever imports, weights, config paths, optional dependencies, or device checks fail.

Routes

• Choose a model, inspect or override YAML, use the registry, or list model families: read sub-skills/model-zoo-and-configuration/SKILL.md.
• Prepare COCO/VOC/WIDER/MOT/keypoint/custom data, convert annotations, validate paths, split semi-supervised data, or slice small-object data: read sub-skills/data-preparation/SKILL.md.
• Train, fine-tune, resume, evaluate, run repository inference, use distributed launch, AMP, VisualDL/W&B, or troubleshoot tools/*.py: read sub-skills/training-evaluation-inference/SKILL.md.
• Export a trained model or deploy with Paddle Inference, Serving, Lite, ONNX, FastDeploy, or TensorRT: read sub-skills/deployment-and-export/SKILL.md.
• Run PP-Human, PP-Vehicle, PP-Tracking, ReID, plate/attribute/action/violation analysis, or image/video/RTSP pipelines: read sub-skills/industrial-pipelines/SKILL.md.

Package baseline

The distribution is paddledet and the primary import is ppdet. The repository's baseline requirements include PaddleDetection utilities plus NumPy below 2, OpenCV, PyYAML, Shapely, SciPy, pycocotools, VisualDL, MOT dependencies, and image augmentation utilities. Install a compatible PaddlePaddle CPU or GPU build first, then install the repository requirements and the package in editable mode when working from source.

Minimal import check:

python -c "import paddle, ppdet; print(paddle.__version__, ppdet.__version__)"
python -c "import paddle; paddle.utils.run_check()"

The source checkout currently advertises package version 0.0.0 even when its Git tag is newer. Treat ppdet.model_zoo.get_config_file() as a network/cache operation and prefer a checked-out config path for source workflows; see the model-zoo route for the observed 404 failure mode.

Shared operating rules

• Configs are YAML and may recursively merge _BASE_ files. Use -o key=value only for deliberate, logged overrides; verify paths and num_classes after merging.
• Weights may be local files or remote URLs. Never download weights or datasets without explicit network approval; record the exact source and checksum when reproducibility matters.
• CPU checks validate parsing/import/data contracts only. They do not prove CUDA, TensorRT, Paddle Lite, Serving, FastDeploy, or vendor accelerator behavior.
• Treat training, benchmark, full pipeline, and TIPC commands as expensive unless a tiny fixture and explicit runtime budget are available.
• Keep generated outputs outside the source tree when possible: checkpoints under a chosen save_dir, inference exports under a separate output_inference-style directory, and visualizations/results under a separate output directory.
• Before changing a config, inspect the dataset reader, metric, input shape, class count, device flags, and pretrained/weights fields together.

Read references/install-and-environment.md for supported dependency variants and backend boundaries, references/verification-candidates.md for safe checks, and references/repo-provenance.md before deciding whether this skill is stale.