rwightman/timm
stale
significant_divergence
Selected 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.
pprp/timm
stale
significant_divergence
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.
rasbt/pytorch-image-models
stale
significant_divergence
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.
neuralmagic/pytorch-image-models
stale
significant_divergence
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.
MohammadAminDHM/pytorch-image-models
stale
significant_divergence
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.
DeGirum/pytorch-image-models-multi-label
stale
significant_divergence
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.
tmp-iclr/pytorch-image-models
stale
significant_divergence
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.
DingXiaoH/pytorch-image-models
stale
significant_divergence
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.
blackpearl1022/pytorch-image-models
stale
significant_divergence
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.
veritable-tech/pytorch-image-models
stale
significant_divergence
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.