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name: inference-and-api description: "Use YOLOX for PyTorch inference, demo CLI/API setup, model selection, checkpoint loading, preprocessing/postprocessing, visualization, and quick diagnostics." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

YOLOX Inference And API

Use this sub-skill when the task is to run YOLOX image, video, or webcam inference, choose a built-in model or custom experiment, load a checkpoint, reproduce the package demo flow in Python, inspect inference-time APIs, or perform a lightweight install/model smoke check.

Route training, evaluation, custom datasets, caching, and experiment logging to ../training-and-data/SKILL.md. Route ONNX, TorchScript, TensorRT, OpenVINO, ncnn, and other deployment backend work to ../export-and-deployment/SKILL.md.

Read/run map

• Read references/inference-workflows.md for package-style demo commands, checkpoint loading expectations, and a distilled Python inference recipe.
• Read references/model-and-api-reference.md for built-in model names, default experiment facts, key signatures, output conventions, and source-script treatment.
• Read references/troubleshooting.md for checkpoint, image/video, device, FP16, legacy-weight, OpenCV, and TensorRT-route failures.
• Run scripts/yolox_inference_smoke.py to validate imports, experiment resolution, model construction, optional CUDA allocation, and optional dummy forward without weights or downloads.

Fast decisions

1. For standard COCO architectures, prefer --name with yolox-s, yolox-m, yolox-l, yolox-x, yolox-tiny, yolox-nano, or yolov3.
2. For a custom class count, custom size, or modified architecture, prefer --exp-file and a checkpoint trained from that same Exp.
3. For visual demos, use installed module commands such as python -m yolox.tools.demo ... rather than checkout script paths.
4. For library use, reproduce the core flow: get_exp, exp.get_model(), load ckpt["model"] or a state dict, model.eval(), optional CUDA/half/fuse, ValTransform, forward, postprocess, and vis.
5. For deployment-specific --trt or exported graphs, stop after identifying the route and hand off to export/deployment.

Minimal safe diagnostic

From this sub-skill directory:

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

Add --dummy-forward only when you want to exercise a real forward pass without weights. Use a small --test-size for CPU-only diagnostics.