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name: models-and-weights description: "Select, construct, configure, inspect, and troubleshoot TorchVision models, pretrained weights, weight transforms, and feature extraction." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

TorchVision Models and Weights

Use this sub-skill when a task asks an agent to choose or instantiate a TorchVision model, handle pretrained weights safely, inspect available model/weight APIs, get the correct inference preprocessing, interpret model-family outputs, or extract intermediate features.

Route Here For

• torchvision.models builders and task subpackages: classification, detection, segmentation, video, quantization, optical flow, and feature extraction.
• Model lookup APIs: list_models, get_model, get_model_builder, get_model_weights, and get_weight.
• WeightsEnum objects, DEFAULT aliases, weights.transforms(), weights.meta, download/cache behavior, and no-network test patterns.
• Detection/segmentation/video/optical-flow model output shape interpretation at the model API level.
• torchvision.models.feature_extraction node listing and create_feature_extractor routing for ResNet/FPN-style backbones.

Route Elsewhere

• For general augmentation pipelines, v2 transforms, TVTensors, boxes/masks/keypoints metadata, and transform migration, use ../transforms-and-tv-tensors/.
• For datasets, data roots, image/video decoding, visualization grids, and drawing utilities, use ../datasets-io-utils/.
• For low-level torchvision.ops box/NMS/ROI APIs and custom op diagnostics, use ../ops-and-detection/.
• For official training/evaluation reference scripts, distributed launches, presets, and long-running recipe commands, use ../training-references/.

Fast Paths

• Select or list models: see references/model-selection.md.
• Use weights without accidental downloads: see references/weights-and-inference.md.
• Extract intermediate features or debug node names: see references/feature-extraction.md.
• Diagnose errors and version/cache issues: see references/troubleshooting.md.
• Inspect the installed API safely: run python scripts/inspect_models.py --help, then python scripts/inspect_models.py list --include 'resnet*' or python scripts/inspect_models.py weights resnet50.

Default Safety Policy

Prefer weights=None in tests, examples, and smoke checks unless the user explicitly requests pretrained weights and accepts network/cache behavior. Inspect weight metadata and preprocessing with get_model_weights() or get_weight() before constructing a pretrained model. Call model.eval() for inference and use weights.transforms() for preprocessing whenever weights are used.