timm
PyTorch Image Models
Decision gist · record as of 2026-08-14
Yes. Timm is a mature, actively maintained library with no known vulnerabilities, permissive licensing, and low install friction. It is the de facto standard for accessing a diverse set of pretrained image models in PyTorch. Install it if you need pretrained models, model zoo access, or a training framework for vision tasks.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires torch and torchvision; model inference requires compatible GPU or CPU with sufficient memory depending on model size.
- Low friction: pure Python wheel with five runtime dependencies (torch, torchvision, pyyaml, huggingface_hub, safetensors).
- Active maintenance—last commit 2026-08-11, release 34 days ago—with a large repository (37066 stars) and consistent updates.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
last release 2026-07-11 (34 days) · last repo commit 2026-08-11 · 37,066 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 15,367,053 downloads/mo, #1,189 on PyPI
Alternatives
Verify before relying
pip install timm
import timm
model = timm.create_model('vit_base_patch16', pretrained=True)
output = model(torch.randn(1, 3, 256, 256))- Specific input image dimensions and memory requirements for different model families.
- Compatibility guarantees across PyTorch versions beyond the tested range (1.13 to 2.9.1).
- Whether all model variants and pretrained weights remain available on Hugging Face Hub.
What it is and what it does
Timm is a model zoo and training framework for PyTorch image models. It bundles hundreds of pretrained architectures—vision transformers (ViT, EVA, DINOv3), efficient CNNs (EfficientNet, ConvNeXt), and hybrid designs—with their weights hosted on Hugging Face Hub. You use it to load a pretrained model in one line, fine-tune it on your data, or extract features for downstream tasks.
The package depends on torch, torchvision, pyyaml, huggingface_hub, and safetensors. It supports Python 3.8–3.12 and is actively maintained; recent releases add new model families (DINOv3, MobileCLIP-2, SigLIP-2), optimizer variants (Muon, NAdaMuon), and infrastructure improvements (meta-device initialization, pickle security hardening). Training and inference scripts are included.
Use it for
- Load a pretrained ViT or ConvNeXt model and fine-tune it on your image classification dataset.
- Extract intermediate feature maps from a pretrained model for use in a downstream vision task.
- Benchmark inference speed and accuracy across multiple model architectures on your hardware.
- Train a custom image model from scratch using timm's training scripts and optimizer support.
- Integrate a pretrained encoder (e.g., DINOv3 ViT) into a multimodal or self-supervised pipeline.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Timm is a mature, actively maintained library with no known vulnerabilities, permissive licensing, and low install friction. It is the de facto standard for accessing a diverse set of pretrained image models in PyTorch. Install it if you need pretrained models, model zoo access, or a training framework for vision tasks.
Install
timm on PyPI
Before you install
Low friction: pure Python wheel with five runtime dependencies (torch, torchvision, pyyaml, huggingface_hub, safetensors). Active maintenance—last commit 2026-08-11, release 34 days ago—with a large repository (37066 stars) and consistent updates.
Requires torch and torchvision; model inference requires compatible GPU or CPU with sufficient memory depending on model size.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
Quickstart
pip install timm
import timm
model = timm.create_model('vit_base_patch16', pretrained=True)
output = model(torch.randn(1, 3, 256, 256))Verify before relying
- Specific input image dimensions and memory requirements for different model families.
- Compatibility guarantees across PyTorch versions beyond the tested range (1.13 to 2.9.1).
- Whether all model variants and pretrained weights remain available on Hugging Face Hub.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagestorchtorchvisionpyyamlhuggingface_hubsafetensors |
| Maintenance | Actively maintained 34 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 15,367,053 / month, #1,189 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: timm-1.0.28-py3-none-any.whl
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See also effdet · segmentation-models-pytorch · vit-pytorch · efficientnet-pytorch · torchxrayvision · pretrainedmodels · transformers · open-clip-torch · torch-ema · pyiqa