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name: training description: "Training, fine-tuning, evaluation, distillation, custom COCO dataset setup, and distributed launch workflows for DAMO-YOLO." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

DAMO-YOLO training

Use this sub-skill when the task involves DAMO-YOLO training, fine-tuning, evaluation, distillation, dataset setup, config editing, checkpoints, or distributed CUDA launch commands.

Do not use this sub-skill for image/video/camera demos, ONNX/TensorRT export, partial quantization, or deployment-only backend diagnostics; route those to the inference or deployment sub-skill.

First actions

1. Identify the workflow: train from scratch, fine-tune from detector weights, resume an interrupted run, evaluate a checkpoint, or run teacher-student distillation.
2. Read the relevant bundled references before writing commands:
   • Training and evaluation workflows
   • Custom COCO dataset setup
   • Config editing guide
   • Training troubleshooting
3. Confirm the runtime has an installed damo package, CUDA-enabled PyTorch, NCCL support, a user-owned config file, and any dataset/checkpoint files needed by that config.
4. Prefer a copied/edited config file for durable changes. In this repo version, nested command-line config overrides are not reliable because Config.merge() only replaces existing top-level attributes.
5. Use --workdir with bundled scripts when the config reads relative TinyNAS structure files or relative dataset paths.

Bundled helper scripts

These scripts adapt the repository train/eval behavior into generated-skill-owned launchers. They import the installed damo package and do not call repo-local tools/train.py or tools/eval.py.

• scripts/launch_train.sh: validates config/dataset/CUDA/NCCL state, then launches bundled train_entrypoint.py through torch.distributed.run; supports single/multi-GPU runs and optional teacher args for distillation.
• scripts/launch_eval.sh: validates config/dataset/checkpoint/CUDA/NCCL state, then launches bundled eval_entrypoint.py through torch.distributed.run; supports --fuse and source-compatible parser flags.
• scripts/validate_coco_config.py: checks that a config, path catalog entries, annotation JSON, class names, and head class count agree before a long run.

Operating rules

• Dataset entries are COCO-only by default. Dataset names must contain coco because the base config routes by substring and raises Only support coco format dataset now! otherwise.
• cfg.dataset.class_names is required. Annotation category names must match these names exactly; category ids may be non-contiguous, but they are remapped by category name into contiguous class indices.
• Training currently supports only one dataset name in cfg.dataset.train_ann; validation may list multiple dataset names.
• cfg.model.head['num_classes'] must equal len(cfg.dataset.class_names) and match the checkpoint head whenever strict loading is used.
• cfg.train.finetune_path starts a new run from pretrained detector weights. cfg.train.resume_path restores model, optimizer, and epoch. If both are set, finetune wins in Trainer.__init__, so never set both for an intended resume.
• For training/eval, choose --gpus equal to the number of visible GPUs and make cfg.train.batch_size or cfg.test.batch_size divisible by that count.