Recent Changes

Jan 5, 2023

ConvNeXt-V2 models and weights added to existing convnext.py
- Paper: ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
- Reference impl: https://github.com/facebookresearch/ConvNeXt-V2 (NOTE: weights currently CC-BY-NC)

Dec 23, 2022 🎄☃

Add FlexiViT models and weights from https://github.com/google-research/big_vision (check out paper at https://arxiv.org/abs/2212.08013)
- NOTE currently resizing is static on model creation, on-the-fly dynamic / train patch size sampling is a WIP
Many more models updated to multi-weight and downloadable via HF hub now (convnext, efficientnet, mobilenet, vision_transformer*, beit)
More model pretrained tag and adjustments, some model names changed (working on deprecation translations, consider main branch DEV branch right now, use 0.6.x for stable use)
More ImageNet-12k (subset of 22k) pretrain models popping up:
- efficientnet_b5.in12k_ft_in1k - 85.9 @ 448x448
- vit_medium_patch16_gap_384.in12k_ft_in1k - 85.5 @ 384x384
- vit_medium_patch16_gap_256.in12k_ft_in1k - 84.5 @ 256x256
- convnext_nano.in12k_ft_in1k - 82.9 @ 288x288

Dec 8, 2022

Add 'EVA l' to vision_transformer.py, MAE style ViT-L/14 MIM pretrain w/ EVA-CLIP targets, FT on ImageNet-1k (w/ ImageNet-22k intermediate for some)
- original source: https://github.com/baaivision/EVA

model	top1	param_count	gmac	macts	hub
eva_large_patch14_336.in22k_ft_in22k_in1k	89.2	304.5	191.1	270.2	link
eva_large_patch14_336.in22k_ft_in1k	88.7	304.5	191.1	270.2	link
eva_large_patch14_196.in22k_ft_in22k_in1k	88.6	304.1	61.6	63.5	link
eva_large_patch14_196.in22k_ft_in1k	87.9	304.1	61.6	63.5	link

Dec 6, 2022

Add 'EVA g', BEiT style ViT-g/14 model weights w/ both MIM pretrain and CLIP pretrain to beit.py.
- original source: https://github.com/baaivision/EVA
- paper: https://arxiv.org/abs/2211.07636

model	top1	param_count	gmac	macts	hub
eva_giant_patch14_560.m30m_ft_in22k_in1k	89.8	1014.4	1906.8	2577.2	link
eva_giant_patch14_336.m30m_ft_in22k_in1k	89.6	1013	620.6	550.7	link
eva_giant_patch14_336.clip_ft_in1k	89.4	1013	620.6	550.7	link
eva_giant_patch14_224.clip_ft_in1k	89.1	1012.6	267.2	192.6	link

Dec 5, 2022

Pre-release (0.8.0dev0) of multi-weight support (model_arch.pretrained_tag). Install with pip install --pre timm
- vision_transformer, maxvit, convnext are the first three model impl w/ support
- model names are changing with this (previous _21k, etc. fn will merge), still sorting out deprecation handling
- bugs are likely, but I need feedback so please try it out
- if stability is needed, please use 0.6.x pypi releases or clone from 0.6.x branch
Support for PyTorch 2.0 compile is added in train/validate/inference/benchmark, use --torchcompile argument
Inference script allows more control over output, select k for top-class index + prob json, csv or parquet output
Add a full set of fine-tuned CLIP image tower weights from both LAION-2B and original OpenAI CLIP models

model	top1	param_count	gmac	macts	hub
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k	88.6	632.5	391	407.5	link
vit_large_patch14_clip_336.openai_ft_in12k_in1k	88.3	304.5	191.1	270.2	link
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k	88.2	632	167.4	139.4	link
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k	88.2	304.5	191.1	270.2	link
vit_large_patch14_clip_224.openai_ft_in12k_in1k	88.2	304.2	81.1	88.8	link
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k	87.9	304.2	81.1	88.8	link
vit_large_patch14_clip_224.openai_ft_in1k	87.9	304.2	81.1	88.8	link
vit_large_patch14_clip_336.laion2b_ft_in1k	87.9	304.5	191.1	270.2	link
vit_huge_patch14_clip_224.laion2b_ft_in1k	87.6	632	167.4	139.4	link
vit_large_patch14_clip_224.laion2b_ft_in1k	87.3	304.2	81.1	88.8	link
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k	87.2	86.9	55.5	101.6	link
vit_base_patch16_clip_384.openai_ft_in12k_in1k	87	86.9	55.5	101.6	link
vit_base_patch16_clip_384.laion2b_ft_in1k	86.6	86.9	55.5	101.6	link
vit_base_patch16_clip_384.openai_ft_in1k	86.2	86.9	55.5	101.6	link
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k	86.2	86.6	17.6	23.9	link
vit_base_patch16_clip_224.openai_ft_in12k_in1k	85.9	86.6	17.6	23.9	link
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k	85.8	88.3	17.9	23.9	link
vit_base_patch16_clip_224.laion2b_ft_in1k	85.5	86.6	17.6	23.9	link
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k	85.4	88.3	13.1	16.5	link
vit_base_patch16_clip_224.openai_ft_in1k	85.3	86.6	17.6	23.9	link
vit_base_patch32_clip_384.openai_ft_in12k_in1k	85.2	88.3	13.1	16.5	link
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k	83.3	88.2	4.4	5	link
vit_base_patch32_clip_224.laion2b_ft_in1k	82.6	88.2	4.4	5	link
vit_base_patch32_clip_224.openai_ft_in1k	81.9	88.2	4.4	5	link

Port of MaxViT Tensorflow Weights from official impl at https://github.com/google-research/maxvit
- There was larger than expected drops for the upscaled 384/512 in21k fine-tune weights, possible detail missing, but the 21k FT did seem sensitive to small preprocessing

model	top1	param_count	gmac	macts	hub
maxvit_xlarge_tf_512.in21k_ft_in1k	88.5	475.8	534.1	1413.2	link
maxvit_xlarge_tf_384.in21k_ft_in1k	88.3	475.3	292.8	668.8	link
maxvit_base_tf_512.in21k_ft_in1k	88.2	119.9	138	704	link
maxvit_large_tf_512.in21k_ft_in1k	88	212.3	244.8	942.2	link
maxvit_large_tf_384.in21k_ft_in1k	88	212	132.6	445.8	link
maxvit_base_tf_384.in21k_ft_in1k	87.9	119.6	73.8	332.9	link
maxvit_base_tf_512.in1k	86.6	119.9	138	704	link
maxvit_large_tf_512.in1k	86.5	212.3	244.8	942.2	link
maxvit_base_tf_384.in1k	86.3	119.6	73.8	332.9	link
maxvit_large_tf_384.in1k	86.2	212	132.6	445.8	link
maxvit_small_tf_512.in1k	86.1	69.1	67.3	383.8	link
maxvit_tiny_tf_512.in1k	85.7	31	33.5	257.6	link
maxvit_small_tf_384.in1k	85.5	69	35.9	183.6	link
maxvit_tiny_tf_384.in1k	85.1	31	17.5	123.4	link
maxvit_large_tf_224.in1k	84.9	211.8	43.7	127.4	link
maxvit_base_tf_224.in1k	84.9	119.5	24	95	link
maxvit_small_tf_224.in1k	84.4	68.9	11.7	53.2	link
maxvit_tiny_tf_224.in1k	83.4	30.9	5.6	35.8	link

Oct 15, 2022

Train and validation script enhancements
Non-GPU (ie CPU) device support
SLURM compatibility for train script
HF datasets support (via ReaderHfds)
TFDS/WDS dataloading improvements (sample padding/wrap for distributed use fixed wrt sample count estimate)
in_chans !=3 support for scripts / loader
Adan optimizer
Can enable per-step LR scheduling via args
Dataset 'parsers' renamed to 'readers', more descriptive of purpose
AMP args changed, APEX via --amp-impl apex, bfloat16 supportedf via --amp-dtype bfloat16
main branch switched to 0.7.x version, 0.6x forked for stable release of weight only adds
master -> main branch rename

Oct 10, 2022

More weights in maxxvit series, incl first ConvNeXt block based coatnext and maxxvit experiments:
- coatnext_nano_rw_224 - 82.0 @ 224 (G) -- (uses ConvNeXt conv block, no BatchNorm)
- maxxvit_rmlp_nano_rw_256 - 83.0 @ 256, 83.7 @ 320 (G) (uses ConvNeXt conv block, no BN)
- maxvit_rmlp_small_rw_224 - 84.5 @ 224, 85.1 @ 320 (G)
- maxxvit_rmlp_small_rw_256 - 84.6 @ 256, 84.9 @ 288 (G) -- could be trained better, hparams need tuning (uses ConvNeXt block, no BN)
- coatnet_rmlp_2_rw_224 - 84.6 @ 224, 85 @ 320 (T)
- NOTE: official MaxVit weights (in1k) have been released at https://github.com/google-research/maxvit -- some extra work is needed to port and adapt since my impl was created independently of theirs and has a few small differences + the whole TF same padding fun.

Sept 23, 2022

LAION-2B CLIP image towers supported as pretrained backbones for fine-tune or features (no classifier)
- vit_base_patch32_224_clip_laion2b
- vit_large_patch14_224_clip_laion2b
- vit_huge_patch14_224_clip_laion2b
- vit_giant_patch14_224_clip_laion2b

Sept 7, 2022

Hugging Face timm docs home now exists, look for more here in the future
Add BEiT-v2 weights for base and large 224x224 models from https://github.com/microsoft/unilm/tree/master/beit2
Add more weights in maxxvit series incl a pico (7.5M params, 1.9 GMACs), two tiny variants:
- maxvit_rmlp_pico_rw_256 - 80.5 @ 256, 81.3 @ 320 (T)
- maxvit_tiny_rw_224 - 83.5 @ 224 (G)
- maxvit_rmlp_tiny_rw_256 - 84.2 @ 256, 84.8 @ 320 (T)

Aug 29, 2022

MaxVit window size scales with img_size by default. Add new RelPosMlp MaxViT weight that leverages this:
- maxvit_rmlp_nano_rw_256 - 83.0 @ 256, 83.6 @ 320 (T)

Aug 26, 2022

CoAtNet (https://arxiv.org/abs/2106.04803) and MaxVit (https://arxiv.org/abs/2204.01697) timm original models
- both found in maxxvit.py model def, contains numerous experiments outside scope of original papers
- an unfinished Tensorflow version from MaxVit authors can be found https://github.com/google-research/maxvit
Initial CoAtNet and MaxVit timm pretrained weights (working on more):
- coatnet_nano_rw_224 - 81.7 @ 224 (T)
- coatnet_rmlp_nano_rw_224 - 82.0 @ 224, 82.8 @ 320 (T)
- coatnet_0_rw_224 - 82.4 (T) -- NOTE timm '0' coatnets have 2 more 3rd stage blocks
- coatnet_bn_0_rw_224 - 82.4 (T)
- maxvit_nano_rw_256 - 82.9 @ 256 (T)
- coatnet_rmlp_1_rw_224 - 83.4 @ 224, 84 @ 320 (T)
- coatnet_1_rw_224 - 83.6 @ 224 (G)
- (T) = TPU trained with bits_and_tpu branch training code, (G) = GPU trained
GCVit (weights adapted from https://github.com/NVlabs/GCVit, code 100% timm re-write for license purposes)
MViT-V2 (multi-scale vit, adapted from https://github.com/facebookresearch/mvit)
EfficientFormer (adapted from https://github.com/snap-research/EfficientFormer)
PyramidVisionTransformer-V2 (adapted from https://github.com/whai362/PVT)
'Fast Norm' support for LayerNorm and GroupNorm that avoids float32 upcast w/ AMP (uses APEX LN if available for further boost)

Aug 15, 2022

ConvNeXt atto weights added
- convnext_atto - 75.7 @ 224, 77.0 @ 288
- convnext_atto_ols - 75.9 @ 224, 77.2 @ 288

Aug 5, 2022

More custom ConvNeXt smaller model defs with weights
- convnext_femto - 77.5 @ 224, 78.7 @ 288
- convnext_femto_ols - 77.9 @ 224, 78.9 @ 288
- convnext_pico - 79.5 @ 224, 80.4 @ 288
- convnext_pico_ols - 79.5 @ 224, 80.5 @ 288
- convnext_nano_ols - 80.9 @ 224, 81.6 @ 288
Updated EdgeNeXt to improve ONNX export, add new base variant and weights from original (https://github.com/mmaaz60/EdgeNeXt)

July 28, 2022

Add freshly minted DeiT-III Medium (width=512, depth=12, num_heads=8) model weights. Thanks Hugo Touvron!

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