CVNets

CVNets is the top-level operating skill for the Apple ml-cvnets repository. Use this skill when a user mentions CVNets, cvnets-* commands, the model zoo, YAML configs under config/, the examples/ recipes, or any of the repo's training, evaluation, model-building, data, or export workflows.

This root skill stays router-like. It points you to the smallest sub-skill that owns the requested workflow and to the shared references/scripts that are safe to use without reopening the source tree.

Start here

• references/repo-provenance.md — repository snapshot and staleness baseline.
• references/repo-routing-metadata.json — scenario metadata used during import.
• references/api-reference.md — verified public APIs, entry points, and signatures.
• references/cli-reference.md — canonical command families and safe wrapper entry points.
• references/configuration.md — YAML flattening, overrides, and common config keys.
• references/model-overview.md — supported task families and model registry notes.
• references/troubleshooting.md — cross-cutting install/import/config/backend failures.
• scripts/check_install.py — safe import/backend smoke.
• scripts/inspect_config.py — resolve and print a config without training.

Route map

Sub-skill	Use it when	Owns	Excludes
training-and-evaluation	You need to train, finetune, resume, or evaluate a CVNets model.	main_train.py, main_eval.py, distributed setup, checkpoints, optimizer/scheduler/loss assembly, classification, detection, and segmentation evaluation flows.	CoreML conversion, model zoo details, and dataset-layout deep dives.
models-and-architectures	You need to choose, inspect, or debug a CVNets model family.	Model registry behavior, architecture families, pretrained loading, ByteFormer, CLIP, audio/bytes model variants, and exportability expectations.	Training loop mechanics and full dataset-layout walkthroughs.
data-and-config	You need to parse, edit, validate, or override configs; set dataset roots; or reason about samplers, collate fns, transforms, tokenizers, or video readers.	YAML config flattening, dotted option semantics, dataset roots, samplers, collate functions, image/audio/video transforms, and text-tokenizer layouts.	Model-family selection and export/runtime conversion.
conversion-and-profiling	You need CoreML conversion, benchmark throughput, or loss-landscape generation.	main_conversion.py, main_benchmark.py, main_loss_landscape.py, JIT/CoreML export, benchmark settings, and loss-landscape parameters.	Training orchestration and dataset-format design.

Shared facts

• Public package name: cvnets.
• Source version captured by this skill: 0.3.
• The repo uses dotted option names in configs and argparse, but CLI flags still use hyphenated spellings such as --common.config-file.
• setup.py registers console scripts for train/eval/conversion, but some installed environments do not resolve the top-level main_* modules correctly. Prefer the bundled wrappers in this skill tree when you need a reliable entry point.
• Most workflows expect a YAML config file plus one or more dataset roots, pretrained weights, or override arguments.
• GPU usage is optional for some smoke checks but required for the repo's full distributed and benchmark-style examples.

Operating notes

• Read the shared references before guessing at a workflow.
• If you are uncertain whether the question is about model selection, data/config, training, or export, start with the route map and then open the nearest sub-skill.
• Keep runtime commands inside the skill tree; do not tell future agents to rely on source checkout paths from this production batch.