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name: training-and-evaluation description: "Routes EasyCV config-driven training, evaluation, validation, and distributed launch workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Training and evaluation

Use this sub-skill when the task is to train, fine-tune, validate, resume, or compare an EasyCV checkpoint against a config-driven pipeline.

It covers the repo's common training front doors:

• python -m easycv.tools.train
• python -m easycv.tools.eval
• easycv.tools.train(...)
• easycv.tools.eval(...)
• easycv.apis.train_model
• easycv.apis.single_gpu_test
• easycv.apis.multi_gpu_test

Read these references first

• references/workflows.md for the training / evaluation command patterns.
• references/config-templates.md for choosing a config family or --model_type starter.
• references/troubleshooting.md for common launcher, seed, fp16, and validation failures.
• Root references/model-zoo-overview.md for the full family map.

What belongs here

Include tasks such as:

• picking a classification, detection, segmentation, pose, SSL, metric-learning, video, or OCR config
• editing dataset roots, work_dir, load_from, resume_from, or pretrained
• running validation hooks and metric evaluation
• distributed launch setup and --launcher selection
• fp16 / SyncBN / seed / logging / visualization setup during training
• training-time result comparison across configs or checkpoints

What stays elsewhere

• Batch prediction and predictor API usage -> sub-skills/prediction-and-inference/
• Export, pruning, quantization, and other inference packaging -> sub-skills/export-and-optimization/
• Dataset conversion, file layout checks, and OSS I/O setup -> sub-skills/data-preparation/

Typical decision flow

1. Choose a config family or explicit config path.
2. Confirm the dataset layout and required labels or class list.
3. Set work_dir, checkpoint resume / load settings, and the launcher.
4. Decide whether fp16, validation, and logging hooks are needed.
5. Run the smallest safe train or eval command first, then expand.

Common training surfaces

• --model_type only works for the starter keys listed in the model-zoo reference.
• --user_config_params overrides config values without editing the source file.
• eval_pipelines controls validation during training and post-training evaluation.
• DALI-backed datasets, OSS I/O, and TorchAccelerator configs have extra runtime expectations.

Common success signals

• The config loads without path or template errors.
• The trainer constructs the model, dataloaders, and hooks.
• Validation metrics are emitted for the chosen dataset family.
• The checkpoint metadata includes the EasyCV version and config text.