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name: model-zoo-inference description: "Operate MMPreTrain model zoo discovery, inference APIs, task inferencers, and feature extraction." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MMPreTrain model zoo and inference router

Use this sub-skill when the user needs to discover packaged MMPreTrain model names, construct a model from the model zoo or a config, run an inference API, extract image features, choose checkpoint/device/offload options, or interpret an inferencer result.

Route by intent

• Find a model name or task family: use references/api-reference.md for list_models, task-filter names, and ModelHub behavior. The bundled scripts/list_models.py helper is safe for local model-zoo discovery and does not download checkpoints.
• Construct a model without weights or with a local/URL checkpoint: use references/api-reference.md#get_model-and-checkpoint-selection and prefer pretrained=False when the user explicitly wants no download.
• Run image classification: use references/inference-recipes.md#image-classification or the bundled scripts/classify_image.py helper. The helper avoids checkpoint downloads unless the caller passes --checkpoint or --use-default-checkpoint.
• Run image retrieval or multimodal retrieval: use references/inference-recipes.md#retrieval-prototypes-caches-and-top-k for prototype, prototype_cache, prepare_batch_size, fast_match, and topk planning.
• Run captioning, VQA, visual grounding, or NLVR: use references/inference-recipes.md#multimodal-inferencers and check optional dependency gates in references/troubleshooting.md.
• Extract features or do no-download model surgery: use references/inference-recipes.md#no-download-model-surgery-and-feature-extraction.
• Debug model-index, checkpoint, device, prototype, visualization, or optional-extra failures: use references/troubleshooting.md.

Boundary and handoff

• Route training commands, config authoring, evaluation, resume/AMP/TTA, and checkpoint production to ../training-and-evaluation/SKILL.md.
• Route dataset annotation schemas, custom datasets, custom registries, transforms, and project modules to ../datasets-and-customization/SKILL.md.
• Route CAM, t-SNE, log/result analysis, FLOPs, checkpoint publishing/conversion, TorchServe, and deployment utilities to ../tools-analysis-and-deployment/SKILL.md.
• Do not ask users to open repository docs, demos, tests, configs, or scripts. Use the bundled references and helpers here, or route to the neighboring sub-skill.

Operational defaults

1. Decide whether the request may download weights before constructing an inferencer. MMPreTrain inferencer classes default to pretrained=True; get_model defaults to pretrained=False.
2. For offline or architecture-only work, use pretrained=False and device='cpu' unless the user chooses a verified accelerator environment.
3. For meaningful predictions, use an explicit local checkpoint path or an allowed URL/default checkpoint. Keep cache/network failures separate from model-name failures.
4. Treat returned dictionaries as convenient summaries. Use return_datasamples=True when the user needs full DataSample tensors/fields for downstream Python logic.
5. Use show_dir instead of show=True in headless sessions.