---

name: timm description: "Use PyTorch Image Models (timm) for model discovery, pretrained loading, transforms/data loaders, training APIs, repository CLI workflows, export/checkpoint interoperability, benchmarking/results, and reusable layers/components." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PyTorch Image Models (timm)

Use this skill when a task involves the timm Python package or the PyTorch Image Models repository: creating vision models, loading pretrained weights, preparing transforms, training or evaluating image classifiers, exporting/checkpointing models, benchmarking model variants, or using timm layers in custom PyTorch code.

Start Here

1. Install or verify the public package: python -m pip install timm for normal use, or python -m pip install -e . when working inside a timm checkout.

2. Run a no-download import check before deeper work:

   python - <<'PY'
   import timm, torch
   model = timm.create_model('resnet18', pretrained=False, num_classes=3)
   output = model(torch.randn(1, 3, 224, 224))
   print(timm.__version__, tuple(output.shape))
   PY
   

3. Use scripts/timm_quick_check.py for a repeatable no-download smoke check covering model creation, data config, transform construction, optimizer, and scheduler setup.

4. Route to the focused sub-skill below before writing long code or commands.

Route By Task

• sub-skills/model-library/: model discovery, create_model, pretrained tags/configs, local checkpoints, classifier/input-channel changes, feature extraction, and model creation troubleshooting.
• sub-skills/data-pipelines/: resolve_data_config, create_transform, create_dataset, create_loader, ImageNet metadata, augmentation, prefetcher/device choices, and NaFlex data settings.
• sub-skills/training-workflows/: optimizer/scheduler/loss APIs, EMA, metrics, task and distillation wrappers, hparam interpretation, and API-level training smoke checks.
• sub-skills/cli-workflows/: dry construction of train.py, validate.py, inference.py, and distributed commands for users working with repository scripts or adapted copies.
• sub-skills/export-and-interoperability/: ONNX export/validation commands, checkpoint cleanup/averaging, Hugging Face Hub and local-dir: loading, Torch Hub notes, and conversion caveats.
• sub-skills/benchmarking-and-results/: bounded benchmark command construction, result CSV interpretation, model metadata lookup, and performance-comparison caveats.
• sub-skills/layers-and-components/: reusable timm layers, classifier heads, pooling, patch embedding, feature wrappers, custom components, and shape validation.

Shared References

• Read references/repo-provenance.md to decide whether this skill matches the source checkout or should be refreshed.
• Read references/package-overview.md for package surfaces, install expectations, optional dependency boundaries, and high-level workflow map.
• Read references/troubleshooting.md for cross-cutting install/import, torch/torchvision, pretrained download, device/backend, and source-script issues.

Shared Scripts

• Run python scripts/timm_quick_check.py --model resnet18 --num-classes 3 to verify the current Python can import timm, create a model, run a small forward pass, resolve preprocessing config, and instantiate optimizer/scheduler objects without pretrained downloads.
• Use sub-skill scripts for focused command generation or component probes; they are linked from the nearest sub-skill SKILL.md.

Safety And Scope

• Prefer pretrained=False for smoke checks; pretrained=True can download weights and may require Hugging Face Hub access or a configured cache.
• The repository root scripts are not normal pip console entry points. Use the CLI sub-skill to build commands only when the user has a timm checkout, a copied script, or an equivalent script path.
• Do not benchmark broad model lists by default. Bound model lists, batch sizes, warmup/iteration counts, devices, and precision choices before running expensive commands.
• For CPU-only environments, pass explicit CPU devices in loaders and scripts; several training/eval defaults target CUDA for performance.
• Keep model transforms tied to model.pretrained_cfg whenever pretrained weights are involved; wrong crop/resize/normalization can silently degrade predictions.

Common First Decisions

• If the user asks “which model should I use?”, start in model-library, then use benchmarking-and-results only if they need performance/result tradeoffs.
• If the user asks “why are predictions wrong?”, check model-library for the exact model/pretrained tag and data-pipelines for transform/crop/normalization alignment.
• If the user asks for a training command, use cli-workflows; if they ask for optimizer/scheduler/loss code inside their own loop, use training-workflows.
• If the user asks for ONNX, safetensors, checkpoint cleanup, or Hub packaging, use export-and-interoperability.
• If the user is building custom architectures or debugging tensor shapes inside blocks, use layers-and-components.