---

name: yolox description: "Use YOLOX for object-detection inference, training/data experiments, and checkpoint export/deployment workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

YOLOX

Use this repo skill when a task involves YOLOX, anchor-free YOLO object detection, YOLOX model selection, PyTorch inference, custom-data training/evaluation, experiment files, checkpoints, ONNX/TorchScript export, or optional deployment runtimes.

YOLOX is a PyTorch object-detection package. This skill is self-contained operating guidance for the versioned package surface; it does not require the original repository checkout.

Start here

1. Read references/installation-and-environment.md when installing YOLOX, checking imports, choosing CPU/CUDA dependencies, or deciding which optional export/deployment packages are needed.
2. Run scripts/check_yolox_install.py for a safe package/API/backend smoke check before expensive work.
3. Route to a sub-skill:
   • sub-skills/inference-and-api/SKILL.md for PyTorch image/video/webcam inference, model selection, checkpoint loading, preprocessing/postprocessing, visualization, and inference API diagnostics.
   • sub-skills/training-and-data/SKILL.md for custom datasets, Exp files, training/evaluation commands, caching, image-size settings, freezing, assignment visualization, and loggers.
   • sub-skills/export-and-deployment/SKILL.md for ONNX/TorchScript export and optional ONNXRuntime, TensorRT, OpenVINO, ncnn, MegEngine, or nebullvm deployment choices.
4. Read references/troubleshooting.md when the failure spans installation, imports, optional dependencies, CUDA, checkpoints, or routing between sub-skills.
5. Read references/repo-provenance.md only when you need the source version, evidence paths, or staleness baseline for this generated skill.

Quick route map

User intent	Route	First action
Run YOLOX on images/video/webcam	inference-and-api	Choose --name for built-ins or --exp-file for custom models; validate install with the inference smoke helper.
Use the Python API in an app	inference-and-api	Follow the distilled get_exp → get_model → ValTransform → forward → postprocess → vis flow.
Train on COCO/VOC/custom data	training-and-data	Inspect the Exp, dataset root, annotations, class count, and image sizes before launching training.
Evaluate or resume a checkpoint	training-and-data	Match checkpoint, Exp, dataset/evaluator, device count, and resume-vs-fine-tune mode.
Export a checkpoint to ONNX/TorchScript	export-and-deployment	Dry-run the bundled export helper, then export with an explicit checkpoint path.
Use TensorRT/OpenVINO/ncnn/MegEngine/nebullvm	export-and-deployment	Treat these as optional stacks; probe SDK/toolchain/runtime availability before claiming support.
Install/import/debug YOLOX	root references	Run scripts/check_yolox_install.py, then use root troubleshooting and the nearest sub-skill.

Minimal install smoke

After installing YOLOX and required dependencies in the target environment:

python scripts/check_yolox_install.py --name yolox-nano --device auto --test-size 64

Use CPU for portable diagnostics:

python scripts/check_yolox_install.py --name yolox-nano --device cpu --test-size 64

The smoke check builds a model and can probe CUDA, but it does not download weights, read datasets, run training, or prove detection accuracy.

Operating boundaries

• Use package/module commands such as python -m yolox.tools.demo, python -m yolox.tools.train, and python -m yolox.tools.eval when the installed package exposes them. Do not rely on source checkout script paths.
• Full inference needs a checkpoint that matches the selected Exp; full training/evaluation needs datasets and usually CUDA resources.
• ONNX and TorchScript export require a trained checkpoint. TensorRT/OpenVINO/ncnn/MegEngine/nebullvm require extra runtime/toolchain packages not covered by the base install.
• Old weights that require --legacy in PyTorch demo/eval are not supported by current deployment demos; use a compatible old YOLOX version for those deployment paths or regenerate weights.

Avoid when

• The task is general object-detection theory with no YOLOX usage, configuration, or package workflow.
• The task targets a different YOLO implementation such as YOLOv5/YOLOv8/Ultralytics unless the user is explicitly migrating to or comparing with YOLOX.
• The user asks for a long benchmark or full training run without providing data, checkpoints, compute budget, and permission.