PyTorch Image Models (timm)
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights - ResNet, ResNeXT, Effic
Our verdict
A usable project with some maintenance caveats. This assessment is derived from GitHub's own repository metrics on 2026-08-30, not from hands-on testing.
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights - ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more. The project is written primarily in Python, released under Apache-2.0, and has 37,108 stars and 5,193 forks on GitHub. 201 contributors have committed to it, and the most recent push was 2026-08-28. The latest tagged release is v1.0.29 (2026-08-28). Figures come from the GitHub API on 2026-08-30 and are refreshed daily; the score below weighs adoption, maintenance, release discipline, contributor breadth, licence clarity and issue hygiene.
Repository
- Licence
- Apache-2.0
- Language
- Python
- Last push
- 2026-08-28
Releases
-
v1.0.29Release v1.0.29 -
v1.0.28Release v1.0.28 -
v1.0.27Release v1.0.27 -
v1.0.26Release v1.0.26 -
v1.0.25Release v1.0.25 -
v1.0.24Release v1.0.24
Pros and cons
Pros
Pricing
- Free tierYes
- LicenceApache-2.0
- Self-hostYes, source is public
- Public price listNot published
PyTorch Image Models (timm) does not publish a web price list. For consumer apps that usually means pricing appears inside the product; for enterprise tools it means talking to sales. Why we do this
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Price points are recorded when a price moves, not on a schedule, so a flat run means the price held. Release cadence comes from GitHub releases. A series appears only once we have watched it for long enough to have more than one observation.
User reviews
No one has reviewed PyTorch Image Models (timm) here yet. Reviews are moderated before they appear, and our own score is editorial and separate from user ratings, though once reviews land they do lift it.
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PyTorch Image Models (timm) alternatives
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Side by side
| Tool | Our score | From | Free tier | Best for | |
|---|---|---|---|---|---|
| PyTorch Image Models (timm) this page | 7.7 | - | Yes | ||
| PEFT | 7.7 | - | Yes | ||
| fastai | 7.4 | - | Yes | ||
| LlamaFactory | 7.3 | - | Yes | ||
| DeepSpeed | 7 | - | Yes | ||
| Hugging Face Datasets | 7 | - | Yes | ||
| Label Studio | 6.7 | - | Yes |
Questions
Is pytorch-image-models free?
Yes. pytorch-image-models advertises a free tier on its own site.
How much does pytorch-image-models cost?
pytorch-image-models does not publish a web price list. For consumer apps that usually means pricing appears inside the product; for enterprise tools it means talking to sales.
Is pytorch-image-models open source?
Yes. pytorch-image-models is a public repository released under Apache-2.0. You can self-host or inspect the source directly.
How is pytorch-image-models scored here?
pytorch-image-models scores 7.7 out of 10. That comes from a desk review of public evidence, which is capped at 8.5 out of 10: public pricing, documentation, maintenance signals and how much we could actually verify. Affiliate relationships are excluded from scoring entirely. The full rubric is published on our methodology page.