---

name: torchvision description: "Use this skill when working with TorchVision models, weights, transforms, TVTensors, datasets, image IO, visualization utilities, vision ops, detection helpers, or official reference training workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

TorchVision Repo Skill

Use this skill for practical TorchVision work: choosing models and pretrained weights, building transform pipelines, preparing datasets and image IO checks, using detection/box operators, and planning official reference training commands safely.

TorchVision is a PyTorch computer-vision library. It provides public Python APIs for model architectures, pretrained weight metadata, datasets, transforms, TVTensor metadata types, image IO, visualization helpers, and vision-specific operators.

Start Here

1. Confirm installation and compatibility with python scripts/check_torchvision_install.py.
2. Use references/package-overview.md for the module map, supported surfaces, and install/runtime assumptions.
3. Use references/troubleshooting.md when imports, compiled ops, downloads, codecs, datasets, or version matching fail.
4. Route to the focused sub-skill below instead of treating this root file as a manual.

Route by Task

• Model selection, pretrained weights, weight transforms, output interpretation, PyTorch Hub, or feature extraction: use sub-skills/models-and-weights/.
• Transform pipelines, v2 migration, TVTensor metadata, boxes/masks/keypoints, random transform behavior, or transform performance: use sub-skills/transforms-and-tv-tensors/.
• Built-in/custom datasets, data roots, ImageFolder, FakeData, image decode/encode, visualization utilities, or no-network fixtures: use sub-skills/datasets-io-utils/.
• Box utilities, NMS, ROI Align/Pool, FPN helpers, detection postprocessing, losses/layers, or custom operator errors: use sub-skills/ops-and-detection/.
• Official reference training/evaluation scripts, distributed command planning, dataset layout requirements, and safe dry-run training plans: use sub-skills/training-references/.

Minimal Import Check

python - <<'PY'
import torch
import torchvision
print('torch', torch.__version__)
print('torchvision', torchvision.__version__)
print('ops loaded', torchvision.extension._has_ops())
PY

If ops loaded is false, pure-Python surfaces may still import, but detection ops such as torchvision.ops.nms and many detection models can fail. Use references/troubleshooting.md and sub-skills/ops-and-detection/references/troubleshooting.md.

Common Safe Defaults

• Use weights=None when tests or examples must avoid network downloads.
• Use weight enums and weights.transforms() for real pretrained inference; do not recreate preprocessing by hand unless the task requires it.
• Prefer torchvision.transforms.v2 for new transform pipelines, especially when samples include boxes, masks, videos, or keypoints.
• Use tiny fixtures, FakeData, and bundled smoke scripts before touching real dataset roots or network downloads.
• Treat reference training scripts as command plans by default; they can require datasets, GPUs, distributed launch, and latest-source compatibility.

Bundled Checks

• scripts/check_torchvision_install.py: verifies import, versions, extension availability, important submodules, and no-download smoke surfaces.
• sub-skills/models-and-weights/scripts/inspect_models.py: lists models/weights and inspects safe model metadata.
• sub-skills/transforms-and-tv-tensors/scripts/smoke_transform_pipeline.py: checks v2 transforms and TVTensor metadata on tiny tensors.
• sub-skills/datasets-io-utils/scripts/check_dataset_io.py: creates a tiny no-network dataset/IO fixture.
• sub-skills/ops-and-detection/scripts/smoke_ops.py: checks small CPU box/NMS/ROI operator behavior.
• sub-skills/training-references/scripts/inspect_reference_args.py: summarizes safe reference-training command families without importing source scripts.

Evidence and Staleness

Read references/repo-provenance.md before trusting this skill for a modified checkout or a new TorchVision release. Refresh the skill if the source commit, public APIs, docs, model catalog, transform semantics, dataset list, compiled ops behavior, or reference scripts changed substantially.