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name: training-and-data description: "Design and validate YOLOX training, data, Exp, caching, logging, and evaluation experiments." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

YOLOX Training And Data

Use this sub-skill when a task involves YOLOX training or evaluation setup, dataset layout, custom Exp files, config overrides, caching, image-size behavior, freezing, assignment visualization, or training loggers. Full training and evaluation are dataset-, checkpoint-, and accelerator-dependent; this sub-skill helps plan and validate them before expensive runs.

Route PyTorch demo inference and postprocess/vis details to ../inference-and-api/SKILL.md. Route ONNX, TorchScript, TensorRT, or deployment exports to ../export-and-deployment/SKILL.md.

Read or run these first

• Read references/training-workflows.md for train/eval commands, distributed flags, image-size controls, freezing, assignment visualization, and TensorBoard/MLflow/W&B logging.
• Read references/data-and-exp-reference.md when preparing COCO/VOC/custom datasets, writing an Exp, using YOLOX_DATADIR, applying opts, or reasoning about caching/default model sizes.
• Read references/troubleshooting.md for annotation, image, class-count, checkpoint, cache, distributed, logger, resume, or evaluator failures.
• Run scripts/inspect_yolox_exp.py before expensive training to load an Exp, inspect fields, and optionally check COCO/VOC paths without starting dataloaders or training.

Fast operating rules

1. Prefer installed module commands such as python -m yolox.tools.train, python -m yolox.tools.eval, and python -m yolox.tools.visualize_assign.
2. Specify exactly one experiment source: -n yolox-s for a packaged default or -f path/to/exp.py for custom data/classes/architecture. If both are supplied to get_exp, the file wins.
3. Keep dataset roots explicit. Set YOLOX_DATADIR for built-in COCO/VOC-style experiments, or set self.data_dir in the Exp for custom COCO layouts.
4. Validate num_classes, input_size, test_size, annotations, image directories, and checkpoint compatibility before multi-GPU or cached runs.
5. Treat --cache as a resource decision: RAM cache is fast but memory-heavy; disk cache needs writable dataset-root storage.
6. Put non-default architecture, dataset, evaluator, augmentation, image-size, freezing, optimizer, or scheduler behavior in an Exp subclass. Use CLI opts only for small typed overrides of existing fields.

Safe Exp inspection

From this sub-skill directory:

python scripts/inspect_yolox_exp.py --name yolox-s --expected-format none
python scripts/inspect_yolox_exp.py --exp-file path/to/exp.py --check-data --expected-format coco

The helper never starts training/evaluation. Missing dataset paths under --check-data are reported as preflight failures so the user can fix paths before launching YOLOX.