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PyTorch Image Models
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 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 hyperparam

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Latest release

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