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huggingface/pytorch-image-models

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cached 2026-03-31T09:49:13.736Z
1mo ago

huggingface/pytorch-image-models

huggingface/pytorch-image-models (timm) is a mature PyTorch computer-vision model library focused on image encoders/backbones, with training, validation, inference, export, and pretrained-weight tooling. It is actively maintained, widely used, and fork-heavy, so forks are most interesting if they build on a large, production-stable model zoo and associated training/inference utilities.

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Stars36,573
Forks5,144
Default branchmain
Last pushed2026-03-23T18:13:40Z
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Prefer upstream unless you specifically need this older pinned snapshot. The fork shows no added functionality and is 158 commits behind, so the main tradeoff is stability of an old state versus missing newer fixes and features.

Choose this fork only if you need its older custom weights or compatibility tweaks and can accept being well behind upstream. For most adopters, upstream timm is the safer choice because it is actively maintained, has newer security and model fixes, and includes many capabilities this fork does not track.

Prefer this fork only if you specifically need its older, customized benchmark/model behavior. If you want active maintenance, newer models, and safer checkpoint handling, upstream is the better default.

Prefer this fork only if SparseML/SparseZoo compatibility is the priority. For general timm usage, upstream is the safer choice because it is much newer, actively maintained, and has accumulated important fixes.

Prefer upstream unless you specifically need this older, heavily modified fork. Adopt this fork only if its local changes are already aligned with your workflow and you are prepared to maintain missing upstream updates yourself.

Choose this fork only if its custom model support matches your workload and you can absorb the maintenance burden. For most adopters, upstream timm is the safer default because this fork is materially behind and appears to have stopped receiving active updates.

Prefer the upstream project for almost any production or up-to-date research use. Choose this fork only if you need its older experimental architecture work or want to build from a 2021 snapshot and are prepared to maintain it yourself.

Prefer this fork only if you need its older customizations; otherwise upstream timm is the safer choice because this fork is stale and substantially diverged.

Choose this fork only if you specifically want its custom model/benchmark work and can accept significant drift from upstream. For most adopters, upstream timm is the safer default because it is much newer, actively maintained, and likely has important fixes this fork lacks.

Choose this fork only if you specifically need its older benchmark/workflow customizations; otherwise upstream is the safer choice because it is much newer, actively maintained, and materially richer in fixes and features.