PyTorch Image Models

• What's New
• Introduction
• Models
• Features
• Results
• Getting Started (Documentation)
• Train, Validation, Inference Scripts
• Awesome PyTorch Resources
• Licenses
• Citing

What's New

November 3, 2026

• reset_classifier() and checkpoint reconstruction: fixes across ViT, EVA, BEiT, NaFlexViT, RepViT and iFormer preserve normalization placement and distillation settings when exporting / reloading models after changing the classifier or pooling. Also fix unpooled PiT export and EfficientFormerV2 with distillation disabled.
• NaFlex: fix ViT / EVA norm placement during conversion, support SwiGLU alignment for DINOv3 / Lingbot checkpoints, and preserve grayscale / non-RGB input channels in data loaders.
• Fix layer-wise LR decay grouping for RepViT, HGNet, MViTv2 and pre-norm ViTs, and InceptionNeXt feature sizes with output_stride=8/16.
• Release 1.0.31.

October 24, 2026

• forward_intermediates() and prune_intermediate_layers() are now supported by every model. Newly added for CSPNet, NFNet, DLA, DenseNet (#2870, thanks ghaith-mq), CoaT, ConViT, ConvMixer, CrossViT, HRNet, Inception-v3, Inception-ResNet-v2, NASNet, PNASNet, SelecSLS, SENet, Sequencer2d, VGG, Visformer, Xception and Aligned Xception, with prune_intermediate_layers() for NaFlexViT and MobileNetV5 encoders. CoaT, ConViT, ConvMixer, CrossViT and Visformer gain features_only support.
• prune_intermediate_layers() fixes
  • prune_head=True removes the whole head (head norm, pre-logits layers, attention pooling) so the pruned model leaves no unused params (DDP w/o find_unused_parameters), except CoaT, CrossViT and HRNet when only some of their parallel scales / branches are taken. Pass prune_head=False to keep it.
  • Pruning a hierarchical model to an earlier stage removes the final norm and head sized for the last stage, forward_features() ends at the last kept feature and num_features / head_hidden_size match it. Pruning to the stem feature removes the stem pooling after it.
  • Fixes for grad checkpointing, stop_early, class token / post network removal, and TorchScript of pruned / feature_cls='getter' models across many families.
• head_hidden_size is the actual pre-logits (pooled) width in all models, including concat pooling (catavgmax) and after reset_classifier(). Pool types a model can't apply now raise at creation / reset instead of being silently ignored or failing in forward.
• Other fixes: VOLO num_classes=0 output is the class token embedding (matching forward_head(pre_logits=True)), vit_pe_core reset_classifier() w/ a non map pool removes the attention pool, pool type export via save_for_hf reads the head's current pooling, and headless (num_classes=0) Swin, SwinV2, DaViT and NextViT checkpoints reload correctly.

October 13, 2026

• Hyperparameter sweeps via a new sweep.py: grid / random search over any train.py argument, one effective --config per job, run on a pool of local GPUs and resumable. WSD sweeps train one stable trunk per hyperparameter combination and branch independent cooldowns from its milestone checkpoints, comparing annealing at several budgets for a fraction of the cost of full runs. See the training docs.
• train.py: --resume auto resumes in place from the experiment's own last.pth.tar (checkpoint history, model_best and summary.csv stay consistent), --stop-epoch N stops without changing the schedule, --save-epochs N [N ...] keeps milestone-N.pth.tar snapshots, and YAML --config values are validated against the script's arguments.
• Deprecate PlateauLRScheduler (--sched plateau). timm schedulers are stateless functions of the epoch or update count, the metric-driven plateau schedule never fit that and its state is not checkpointed, so resume was never exact. Use cosine or wsd.
• Docs: new Model API page, generated model family pages for every model module replacing the old per-model pages, and expanded data / loss / models reference pages.
• Model API consistency fixes: reset_classifier() keeps the current pooling in all models, unknown constructor args raise TypeError everywhere, Swin family dynamic_img_size, LeViT layer-wise lr decay, no_weight_decay names that matched no params (NesT, SwinV2, XCiT, FocalNet), resize state recorded for all set_input_size families.
• Fixes: GCViT global query mixed images within a batch, gradient checkpointing for RDNet and MobileNetV5, save_for_hf stale test input size, EfficientViT (MSRA) rectangular resize, ImageDataset image conversion errors (#2846). reparameterize_model moved to timm.models.

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