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name: models-and-architectures description: "Choose, inspect, and debug CVNets model families." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

Models and Architectures

Use this sub-skill when the user wants help choosing a CVNets model family, debugging a registry lookup, understanding how pretrained weights are loaded, or checking whether a model is exportable.

This sub-skill owns the model registry surface, the task-family map, the special cases around ByteFormer, CLIP, and audio/bytes workflows, and the expectations around pretrained loading and exportability. It does not own the full training loop or the dataset/config layout.

Read these first

• ../../references/api-reference.md — verified get_model and registry entry points.
• ../../references/configuration.md — the keys that select a model family.
• ../../references/model-overview.md — the repo-wide family summary.
• references/model-overview.md — deeper family notes and common shape expectations.
• references/troubleshooting.md — registry, pretrained, tokenizer, and export failures.
• scripts/check_model_build.py — safe model-build smoke from a config file.

Owns

• Model-family selection and registry lookups.
• Pretrained model loading and reserved-name handling.
• ByteFormer, CLIP, audio classification, and other model families that are not just plain image backbones.
• Model-info and exportability expectations.
• Shape, class-count, and head/backbone compatibility questions.

Excludes

• Training orchestration, DDP, checkpointing, and optimizer setup; route to training-and-evaluation.
• Dataset roots, sampler names, collate functions, tokenizers, and augmentation pipelines; route to data-and-config.
• CoreML conversion, benchmark throughput, and loss-landscape generation; route to conversion-and-profiling.

Workflow

1. Identify the task category and the intended model.<category>.name value.
2. Check whether the model is a pure backbone, a task head, or a multimodal/audio family with extra inputs.
3. Confirm whether pretrained loading is intended and whether the checkpoint matches the head class count.
4. Use scripts/check_model_build.py for a safe registry/build smoke before guessing at the failure.
5. If the issue is really a config or dataset problem, hand off to data-and-config rather than forcing the model route.

Common signals

• get_model uses the dataset category and model-name pair from the config.
• model.info() is the normal model summary path in the training/evaluation flows.
• __base__ is a registry-only placeholder, not a final user-facing model name.
• Some families need additional tokenizer, byte-encoding, or export-specific inputs in addition to an image tensor.

When to switch away

• If the config file itself is the problem, switch to data-and-config.
• If the user needs an actual train/eval run, switch to training-and-evaluation.
• If the user needs export, throughput, or loss-landscape commands, switch to conversion-and-profiling.