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name: data-and-config description: "Parse, edit, and validate CVNets configs and data layouts." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

Data and Config

Use this sub-skill when the user needs to understand or change a CVNets YAML config, dataset root, sampler, collate function, transform pipeline, tokenizer, or video-reader layout.

This sub-skill owns the dotted-option conventions, config loading, dataset registration, sampler and collate selection, augmentation plumbing, tokenizer settings, and the data formats behind image, audio, video, ByteFormer, and CLIP workflows. It does not own the training loop or the model-family choice itself.

Read these first

• ../../references/api-reference.md — parser and loader contracts.
• ../../references/configuration.md — common config sections and override rules.
• ../../references/model-overview.md — which dataset.category points at which model family.
• references/data-formats.md — dataset, tokenizer, byte, and video layout details.
• references/troubleshooting.md — config, dataset-root, tokenizer, and optional-backend failures.
• ../../scripts/inspect_config.py — inspect a config without running training.
• ../../scripts/check_install.py — confirm optional audio/video/export dependencies.

Owns

• YAML config structure and dotted-key semantics.
• Dataset roots, split names, and dataset/category registration.
• Samplers, collate functions, transforms, and tokenizers.
• Image-text zero-shot layouts, byte-based image/audio recipes, and video-reader settings.
• Quick config inspection and option-override validation.

Excludes

• Choosing or debugging the architecture family; route to models-and-architectures.
• Training, resume, finetuning, or evaluation orchestration; route to training-and-evaluation.
• CoreML conversion, benchmark throughput, and loss-landscape generation; route to conversion-and-profiling.

Workflow

1. Inspect the config with scripts/inspect_config.py before touching the command line.
2. Confirm dataset.category, dataset.name, the relevant dataset.root_* fields, and the matching model.<category>.name.
3. Choose the sampler and collate names that match the modal data path.
4. Check whether the workflow depends on CLIP tokenizer files, byte encodings, PyAV/decord, or other optional extras.
5. If the config is the only problem, fix it here before involving the training or model sub-skill.

Common signals

• load_config_file flattens YAML into dotted keys and warns on unexpected entries.
• --common.override-kwargs is for small targeted overrides, not for inventing new registry entries.
• dataset.collate_fn_name_* controls the collate function for train, val, and test.
• sampler.name and the sampler.bs.* or sampler.vbs.* settings determine the batch and crop behavior.
• text_tokenizer.clip.*, video_reader.*, image_augmentation.*, audio_augmentation.*, and dataset.root_* are the most common locations for modality-specific mistakes.

When to switch away

• If the config is fine and the failure is in the model family, switch to models-and-architectures.
• If the config is fine and the failure is in the training or evaluation loop, switch to training-and-evaluation.
• If the config is fine and the failure is in conversion or profiling, switch to conversion-and-profiling.