---

name: damo-yolo description: "Training, inference, and deployment workflows for DAMO-YOLO object detection." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

DAMO-YOLO

Use this repo skill for DAMO-YOLO object detection workflows built around the installed damo package. The main user-facing routes are training, demo inference, and deployment/export. This skill is self-contained: it bundles the reusable launchers, references, and smoke checks future agents need without reopening the original repository checkout.

Start here

1. Install or verify the damo package in the active environment. For a local checkout, the usual command is pip install -e . because the package metadata reads requirements.txt and installs the damo distribution from this source tree.
2. Run scripts/check_model_smoke.py --config <config.py> --workdir <dir> when you need a quick import/model/config sanity check before a longer workflow.
3. Read references/workflows.md for the route map and bundled helper scripts.
4. Read references/troubleshooting.md if the task involves a missing dependency, config mismatch, class-count error, or relative-path failure.
5. Read references/model-overview.md and references/api-reference.md when you need architecture or API details that are not obvious from the sub-skill router.

Route map

training

Use for training, fine-tuning, resume, evaluation, teacher-student distillation, custom COCO dataset setup, config editing, and distributed CUDA/NCCL launch commands.

Read:

• sub-skills/training/SKILL.md
• sub-skills/training/references/training-workflows.md
• sub-skills/training/references/custom-coco-datasets.md
• sub-skills/training/references/config-editing.md
• sub-skills/training/references/troubleshooting.md

Bundled helpers:

• sub-skills/training/scripts/launch_train.sh
• sub-skills/training/scripts/launch_eval.sh
• sub-skills/training/scripts/validate_coco_config.py

inference

Use for image, video, and camera demo inference with Torch, ONNX, or TensorRT engines, including engine selection and visualization behavior.

Read:

• sub-skills/inference/SKILL.md
• sub-skills/inference/references/engine-and-data-flow.md
• sub-skills/inference/references/demo-workflows.md
• sub-skills/inference/references/source-decisions.md
• sub-skills/inference/references/troubleshooting.md

Bundled helper:

• sub-skills/inference/scripts/damo_yolo_safe_demo.py

deployment

Use for ONNX export, TensorRT export/evaluation planning, partial INT8 quantization guidance, and deployment backend readiness checks.

Read:

• sub-skills/deployment/SKILL.md
• sub-skills/deployment/references/workflows.md
• sub-skills/deployment/references/cli-reference.md
• sub-skills/deployment/references/source-decisions.md
• sub-skills/deployment/references/troubleshooting.md

Bundled helpers:

• sub-skills/deployment/scripts/check_deploy_env.py
• sub-skills/deployment/scripts/export_onnx_safe.py

Routing cues

• If the prompt says train, fine-tune, resume, evaluate, distill, or prepare a COCO dataset, choose training.
• If the prompt says demo, image, video, camera, OpenCV visualization, Torch engine, ONNX Runtime, or TensorRT inference, choose inference.
• If the prompt says export, ONNX, TensorRT, engine build, partial quantization, ONNX Runtime NMS, or backend dependency check, choose deployment.
• If the prompt is only asking whether the environment, package install, config file, or model shape is sane before a long run, use scripts/check_model_smoke.py plus the relevant sub-skill.

Core constraints to remember

• COCO-style datasets are expected by the training pipeline; dataset names must contain coco.
• cfg.dataset.class_names and cfg.model.head.num_classes must agree.
• Relative TinyNAS structure reads need --workdir or absolute config paths.
• Config.merge() only handles exact top-level keys reliably; prefer edited config files for nested changes.
• Training and evaluation require CUDA/NCCL.
• Torch-based inference can fall back to CPU, but ONNX Runtime and TensorRT still depend on their own optional runtimes.
• TensorRT and partial INT8 quantization are optional backend paths; do not claim them unless the active environment actually provides the required packages and libraries.