pytorch-image-models/docs/changes.md

Recent Changes

July 27, 2022

• All runtime benchmark and validation result csv files are up-to-date!
• A few more weights & model defs added:
  • darknetaa53 - 79.8 @ 256, 80.5 @ 288
  • convnext_nano - 80.8 @ 224, 81.5 @ 288
  • cs3sedarknet_l - 81.2 @ 256, 81.8 @ 288
  • cs3darknet_x - 81.8 @ 256, 82.2 @ 288
  • cs3sedarknet_x - 82.2 @ 256, 82.7 @ 288
  • cs3edgenet_x - 82.2 @ 256, 82.7 @ 288
  • cs3se_edgenet_x - 82.8 @ 256, 83.5 @ 320
• cs3* weights above all trained on TPU w/ bits_and_tpu branch. Thanks to TRC program!
• Add output_stride=8 and 16 support to ConvNeXt (dilation)
• deit3 models not being able to resize pos_emb fixed
• Version 0.6.7 PyPi release (/w above bug fixes and new weighs since 0.6.5)

July 8, 2022

More models, more fixes

• Official research models (w/ weights) added:
  • EdgeNeXt from (https://github.com/mmaaz60/EdgeNeXt)
  • MobileViT-V2 from (https://github.com/apple/ml-cvnets)
  • DeiT III (Revenge of the ViT) from (https://github.com/facebookresearch/deit)
• My own models:
  • Small ResNet defs added by request with 1 block repeats for both basic and bottleneck (resnet10 and resnet14)
  • CspNet refactored with dataclass config, simplified CrossStage3 (cs3) option. These are closer to YOLO-v5+ backbone defs.
  • More relative position vit fiddling. Two srelpos (shared relative position) models trained, and a medium w/ class token.
  • Add an alternate downsample mode to EdgeNeXt and train a small model. Better than original small, but not their new USI trained weights.
• My own model weight results (all ImageNet-1k training)
  • resnet10t - 66.5 @ 176, 68.3 @ 224
  • resnet14t - 71.3 @ 176, 72.3 @ 224
  • resnetaa50 - 80.6 @ 224 , 81.6 @ 288
  • darknet53 - 80.0 @ 256, 80.5 @ 288
  • cs3darknet_m - 77.0 @ 256, 77.6 @ 288
  • cs3darknet_focus_m - 76.7 @ 256, 77.3 @ 288
  • cs3darknet_l - 80.4 @ 256, 80.9 @ 288
  • cs3darknet_focus_l - 80.3 @ 256, 80.9 @ 288
  • vit_srelpos_small_patch16_224 - 81.1 @ 224, 82.1 @ 320
  • vit_srelpos_medium_patch16_224 - 82.3 @ 224, 83.1 @ 320
  • vit_relpos_small_patch16_cls_224 - 82.6 @ 224, 83.6 @ 320
  • edgnext_small_rw - 79.6 @ 224, 80.4 @ 320
• cs3, darknet, and vit_*relpos weights above all trained on TPU thanks to TRC program! Rest trained on overheating GPUs.
• Hugging Face Hub support fixes verified, demo notebook TBA
• Pretrained weights / configs can be loaded externally (ie from local disk) w/ support for head adaptation.
• Add support to change image extensions scanned by timm datasets/parsers. See (https://github.com/rwightman/pytorch-image-models/pull/1274#issuecomment-1178303103)
• Default ConvNeXt LayerNorm impl to use F.layer_norm(x.permute(0, 2, 3, 1), ...).permute(0, 3, 1, 2) via LayerNorm2d in all cases.
  • a bit slower than previous custom impl on some hardware (ie Ampere w/ CL), but overall fewer regressions across wider HW / PyTorch version ranges.
  • previous impl exists as LayerNormExp2d in models/layers/norm.py
• Numerous bug fixes
• Currently testing for imminent PyPi 0.6.x release
• LeViT pretraining of larger models still a WIP, they don't train well / easily without distillation. Time to add distill support (finally)?
• ImageNet-22k weight training + finetune ongoing, work on multi-weight support (slowly) chugging along (there are a LOT of weights, sigh) ...

May 13, 2022

• Official Swin-V2 models and weights added from (https://github.com/microsoft/Swin-Transformer). Cleaned up to support torchscript.
• Some refactoring for existing timm Swin-V2-CR impl, will likely do a bit more to bring parts closer to official and decide whether to merge some aspects.
• More Vision Transformer relative position / residual post-norm experiments (all trained on TPU thanks to TRC program)
  • vit_relpos_small_patch16_224 - 81.5 @ 224, 82.5 @ 320 -- rel pos, layer scale, no class token, avg pool
  • vit_relpos_medium_patch16_rpn_224 - 82.3 @ 224, 83.1 @ 320 -- rel pos + res-post-norm, no class token, avg pool
  • vit_relpos_medium_patch16_224 - 82.5 @ 224, 83.3 @ 320 -- rel pos, layer scale, no class token, avg pool
  • vit_relpos_base_patch16_gapcls_224 - 82.8 @ 224, 83.9 @ 320 -- rel pos, layer scale, class token, avg pool (by mistake)
• Bring 512 dim, 8-head 'medium' ViT model variant back to life (after using in a pre DeiT 'small' model for first ViT impl back in 2020)
• Add ViT relative position support for switching btw existing impl and some additions in official Swin-V2 impl for future trials
• Sequencer2D impl (https://arxiv.org/abs/2205.01972), added via PR from author (https://github.com/okojoalg)

May 2, 2022

• Vision Transformer experiments adding Relative Position (Swin-V2 log-coord) (vision_transformer_relpos.py) and Residual Post-Norm branches (from Swin-V2) (vision_transformer*.py)
  • vit_relpos_base_patch32_plus_rpn_256 - 79.5 @ 256, 80.6 @ 320 -- rel pos + extended width + res-post-norm, no class token, avg pool
  • vit_relpos_base_patch16_224 - 82.5 @ 224, 83.6 @ 320 -- rel pos, layer scale, no class token, avg pool
  • vit_base_patch16_rpn_224 - 82.3 @ 224 -- rel pos + res-post-norm, no class token, avg pool
• Vision Transformer refactor to remove representation layer that was only used in initial vit and rarely used since with newer pretrain (ie How to Train Your ViT)
• vit_* models support removal of class token, use of global average pool, use of fc_norm (ala beit, mae).

April 22, 2022

• timm models are now officially supported in fast.ai! Just in time for the new Practical Deep Learning course. timmdocs documentation link updated to timm.fast.ai.
• Two more model weights added in the TPU trained series. Some In22k pretrain still in progress.
  • seresnext101d_32x8d - 83.69 @ 224, 84.35 @ 288
  • seresnextaa101d_32x8d (anti-aliased w/ AvgPool2d) - 83.85 @ 224, 84.57 @ 288

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

• ResNet strikes back (https://arxiv.org/abs/2110.00476) weights added, plus any extra training components used. Model weights and some more details here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-rsb-weights)
• BCE loss and Repeated Augmentation support for RSB paper
• 4 series of ResNet based attention model experiments being added (implemented across byobnet.py/byoanet.py). These include all sorts of attention, from channel attn like SE, ECA to 2D QKV self-attention layers such as Halo, Bottlneck, Lambda. Details here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-attn-weights)
• Working implementations of the following 2D self-attention modules (likely to be differences from paper or eventual official impl):
  • Halo (https://arxiv.org/abs/2103.12731)
  • Bottleneck Transformer (https://arxiv.org/abs/2101.11605)
  • LambdaNetworks (https://arxiv.org/abs/2102.08602)
• A RegNetZ series of models with some attention experiments (being added to). These do not follow the paper (https://arxiv.org/abs/2103.06877) in any way other than block architecture, details of official models are not available. See more here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-attn-weights)
• ConvMixer (https://openreview.net/forum?id=TVHS5Y4dNvM), CrossVit (https://arxiv.org/abs/2103.14899), and BeiT (https://arxiv.org/abs/2106.08254) architectures + weights added
• freeze/unfreeze helpers by Alexander Soare

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.