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Recent Changes

March 23, 2022

  • Add ParallelBlock and LayerScale option to base vit models to support model configs in Three things everyone should know about ViT
  • convnext_tiny_hnf (head norm first) weights trained with (close to) A2 recipe, 82.2% top-1, could do better with more epochs.

March 21, 2022

  • Merge norm_norm_norm. IMPORTANT this update for a coming 0.6.x release will likely de-stabilize the master branch for a while. Branch 0.5.x or a previous 0.5.x release can be used if stability is required.
  • Significant weights update (all TPU trained) as described in this release
    • regnety_040 - 82.3 @ 224, 82.96 @ 288
    • regnety_064 - 83.0 @ 224, 83.65 @ 288
    • regnety_080 - 83.17 @ 224, 83.86 @ 288
    • regnetv_040 - 82.44 @ 224, 83.18 @ 288 (timm pre-act)
    • regnetv_064 - 83.1 @ 224, 83.71 @ 288 (timm pre-act)
    • regnetz_040 - 83.67 @ 256, 84.25 @ 320
    • regnetz_040h - 83.77 @ 256, 84.5 @ 320 (w/ extra fc in head)
    • resnetv2_50d_gn - 80.8 @ 224, 81.96 @ 288 (pre-act GroupNorm)
    • resnetv2_50d_evos 80.77 @ 224, 82.04 @ 288 (pre-act EvoNormS)
    • regnetz_c16_evos - 81.9 @ 256, 82.64 @ 320 (EvoNormS)
    • regnetz_d8_evos - 83.42 @ 256, 84.04 @ 320 (EvoNormS)
    • xception41p - 82 @ 299 (timm pre-act)
    • xception65 - 83.17 @ 299
    • xception65p - 83.14 @ 299 (timm pre-act)
    • resnext101_64x4d - 82.46 @ 224, 83.16 @ 288
    • seresnext101_32x8d - 83.57 @ 224, 84.270 @ 288
    • resnetrs200 - 83.85 @ 256, 84.44 @ 320
  • HuggingFace hub support fixed w/ initial groundwork for allowing alternative 'config sources' for pretrained model definitions and weights (generic local file / remote url support soon)
  • SwinTransformer-V2 implementation added. Submitted by Christoph Reich. Training experiments and model changes by myself are ongoing so expect compat breaks.
  • Swin-S3 (AutoFormerV2) models / weights added from https://github.com/microsoft/Cream/tree/main/AutoFormerV2
  • MobileViT models w/ weights adapted from https://github.com/apple/ml-cvnets
  • PoolFormer models w/ weights adapted from https://github.com/sail-sg/poolformer
  • VOLO models w/ weights adapted from https://github.com/sail-sg/volo
  • Significant work experimenting with non-BatchNorm norm layers such as EvoNorm, FilterResponseNorm, GroupNorm, etc
  • Enhance support for alternate norm + act ('NormAct') layers added to a number of models, esp EfficientNet/MobileNetV3, RegNet, and aligned Xception
  • Grouped conv support added to EfficientNet family
  • Add 'group matching' API to all models to allow grouping model parameters for application of 'layer-wise' LR decay, lr scale added to LR scheduler
  • Gradient checkpointing support added to many models
  • forward_head(x, pre_logits=False) fn added to all models to allow separate calls of forward_features + forward_head
  • All vision transformer and vision MLP models update to return non-pooled / non-token selected features from foward_features, for consistency with CNN models, token selection or pooling now applied in forward_head

Feb 2, 2022

  • Chris Hughes posted an exhaustive run through of timm on his blog yesterday. Well worth a read. Getting Started with PyTorch Image Models (timm): A Practitioner’s Guide
  • I'm currently prepping to merge the norm_norm_norm branch back to master (ver 0.6.x) in next week or so.
    • The changes are more extensive than usual and may destabilize and break some model API use (aiming for full backwards compat). So, beware pip install git+https://github.com/rwightman/pytorch-image-models installs!
    • 0.5.x releases and a 0.5.x branch will remain stable with a cherry pick or two until dust clears. Recommend sticking to pypi install for a bit if you want stable.

Jan 14, 2022

  • Version 0.5.4 w/ release to be pushed to pypi. It's been a while since last pypi update and riskier changes will be merged to main branch soon....
  • Add ConvNeXT models /w weights from official impl (https://github.com/facebookresearch/ConvNeXt), a few perf tweaks, compatible with timm features
  • Tried training a few small (~1.8-3M param) / mobile optimized models, a few are good so far, more on the way...
    • mnasnet_small - 65.6 top-1
    • mobilenetv2_050 - 65.9
    • lcnet_100/075/050 - 72.1 / 68.8 / 63.1
    • semnasnet_075 - 73
    • fbnetv3_b/d/g - 79.1 / 79.7 / 82.0
  • TinyNet models added by rsomani95
  • LCNet added via MobileNetV3 architecture

Nov 22, 2021

  • A number of updated weights anew new model defs
    • eca_halonext26ts - 79.5 @ 256
    • resnet50_gn (new) - 80.1 @ 224, 81.3 @ 288
    • resnet50 - 80.7 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, weights)
    • resnext50_32x4d - 81.1 @ 224, 82.0 @ 288
    • sebotnet33ts_256 (new) - 81.2 @ 224
    • lamhalobotnet50ts_256 - 81.5 @ 256
    • halonet50ts - 81.7 @ 256
    • halo2botnet50ts_256 - 82.0 @ 256
    • resnet101 - 82.0 @ 224, 82.8 @ 288
    • resnetv2_101 (new) - 82.1 @ 224, 83.0 @ 288
    • resnet152 - 82.8 @ 224, 83.5 @ 288
    • regnetz_d8 (new) - 83.5 @ 256, 84.0 @ 320
    • regnetz_e8 (new) - 84.5 @ 256, 85.0 @ 320
  • vit_base_patch8_224 (85.8 top-1) & in21k variant weights added thanks Martins Bruveris
  • Groundwork in for FX feature extraction thanks to Alexander Soare
    • models updated for tracing compatibility (almost full support with some distlled transformer exceptions)

Oct 19, 2021

Aug 18, 2021

  • Optimizer bonanza!
    • Add LAMB and LARS optimizers, incl trust ratio clipping options. Tweaked to work properly in PyTorch XLA (tested on TPUs w/ timm bits branch)
    • Add MADGRAD from FB research w/ a few tweaks (decoupled decay option, step handling that works with PyTorch XLA)
    • Some cleanup on all optimizers and factory. No more .data, a bit more consistency, unit tests for all!
    • SGDP and AdamP still won't work with PyTorch XLA but others should (have yet to test Adabelief, Adafactor, Adahessian myself).
  • EfficientNet-V2 XL TF ported weights added, but they don't validate well in PyTorch (L is better). The pre-processing for the V2 TF training is a bit diff and the fine-tuned 21k -> 1k weights are very sensitive and less robust than the 1k weights.
  • Added PyTorch trained EfficientNet-V2 'Tiny' w/ GlobalContext attn weights. Only .1-.2 top-1 better than the SE so more of a curiosity for those interested.

July 12, 2021

July 5-9, 2021

  • Add efficientnetv2_rw_t weights, a custom 'tiny' 13.6M param variant that is a bit better than (non NoisyStudent) B3 models. Both faster and better accuracy (at same or lower res)
    • top-1 82.34 @ 288x288 and 82.54 @ 320x320
  • Add SAM pretrained in1k weight for ViT B/16 (vit_base_patch16_sam_224) and B/32 (vit_base_patch32_sam_224) models.
  • Add 'Aggregating Nested Transformer' (NesT) w/ weights converted from official Flax impl. Contributed by Alexander Soare.
    • jx_nest_base - 83.534, jx_nest_small - 83.120, jx_nest_tiny - 81.426

June 23, 2021

  • Reproduce gMLP model training, gmlp_s16_224 trained to 79.6 top-1, matching paper. Hparams for this and other recent MLP training here

June 20, 2021

  • Release Vision Transformer 'AugReg' weights from How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
    • .npz weight loading support added, can load any of the 50K+ weights from the AugReg series
    • See example notebook from official impl for navigating the augreg weights
    • Replaced all default weights w/ best AugReg variant (if possible). All AugReg 21k classifiers work.
      • Highlights: vit_large_patch16_384 (87.1 top-1), vit_large_r50_s32_384 (86.2 top-1), vit_base_patch16_384 (86.0 top-1)
    • vit_deit_* renamed to just deit_*
    • Remove my old small model, replace with DeiT compatible small w/ AugReg weights
  • Add 1st training of my gmixer_24_224 MLP /w GLU, 78.1 top-1 w/ 25M params.
  • Add weights from official ResMLP release (https://github.com/facebookresearch/deit)
  • Add eca_nfnet_l2 weights from my 'lightweight' series. 84.7 top-1 at 384x384.
  • Add distilled BiT 50x1 student and 152x2 Teacher weights from Knowledge distillation: A good teacher is patient and consistent
  • NFNets and ResNetV2-BiT models work w/ Pytorch XLA now
    • weight standardization uses F.batch_norm instead of std_mean (std_mean wasn't lowered)
    • eps values adjusted, will be slight differences but should be quite close
  • Improve test coverage and classifier interface of non-conv (vision transformer and mlp) models
  • Cleanup a few classifier / flatten details for models w/ conv classifiers or early global pool
  • Please report any regressions, this PR touched quite a few models.