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name: training-and-evaluation description: "Train, finetune, resume, and evaluate CVNets models." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

Training and Evaluation

Use this sub-skill when the user wants to train, finetune, resume, or evaluate a CVNets model, including the repo's classification, detection, segmentation, CLIP, ByteFormer, and RangeAugment-flavored training flows.

This sub-skill owns the orchestration path around main_train.py, main_eval.py, the distributed setup, checkpoint handling, optimizer/scheduler/loss assembly, and the task-specific evaluation branches for detection and segmentation. It does not own model-family selection details, dataset-layout deep dives, or export/profiling commands.

Read these first

• ../../references/api-reference.md — verified public entry points and signatures.
• ../../references/configuration.md — dotted keys, override rules, and common config sections.
• ../../references/model-overview.md — model-family selection and registry notes.
• references/workflows.md — command patterns for train, eval, resume, finetune, and DDP.
• references/troubleshooting.md — training-specific failures and recovery steps.
• scripts/cvnets_train.py — bundled training wrapper.
• scripts/cvnets_eval.py — bundled generic evaluation wrapper.
• scripts/cvnets_eval_det.py — bundled detection-evaluation wrapper.
• scripts/cvnets_eval_seg.py — bundled segmentation-evaluation wrapper.

Owns

• Single-node and distributed training runs.
• Resume, auto-resume, and finetuning flows.
• Generic evaluation and task-specific evaluation for detection and segmentation.
• Device setup, DDP launch behavior, batch-size adjustments, EMA, and checkpoint save/load.
• Loss, optimizer, and scheduler creation from a parsed config.

Excludes

• Choosing which architecture family to use; route to models-and-architectures.
• Editing YAML keys, dataset roots, sampler names, tokenizer settings, or modality layouts; route to data-and-config.
• CoreML conversion, benchmark throughput, or loss-landscape generation; route to conversion-and-profiling.

Workflow

1. Inspect the config with scripts/inspect_config.py if the run depends on a specific YAML file or override.
2. Decide whether the user needs training, evaluation, resume, or finetuning.
3. Run the matching wrapper with --repo-root <repo-root> so the checkout is added to sys.path and relative config paths resolve against the repo root.
4. For training, make sure dataset.category, model.<category>.name, dataset.root_*, sampler.name, and the optimizer/scheduler sections are consistent.
5. For evaluation, confirm the correct specialization: cvnets_eval.py for generic runs, cvnets_eval_det.py for detection, and cvnets_eval_seg.py for segmentation.
6. If the run fails, check the training-specific troubleshooting file before guessing at model or data problems.

Common signals

• main_train.py prints the parsed options and creates an experiment directory under common.results_loc/common.run_label.
• Trainer owns the epoch/iteration loop and the main training metrics.
• main_eval.py routes detection and segmentation through specialized engine helpers because those tasks save outputs differently.
• common.override-kwargs is the fast path for one-off overrides such as dataset.root_val=... or model.classification.finetune_pretrained_model=false.

When to switch away

• If the user is asking which encoder/head family to pick, switch to models-and-architectures.
• If the issue is actually a missing dataset root, a wrong sampler, or a tokenizer/video-reader layout, switch to data-and-config.
• If the issue is CoreML, JIT, benchmarking, or loss-landscape generation, switch to conversion-and-profiling.