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

July 7, 2025

MobileNet-v5 backbone tweaks for improved Google Gemma 3n behaviour (to pair with updated official weights)
- Add stem bias (zero'd in updated weights, compat break with old weights)
- GELU -> GELU (tanh approx). A minor change to be closer to JAX
Add two arguments to layer-decay support, a min scale clamp and 'no optimization' scale threshold
Add 'Fp32' LayerNorm, RMSNorm, SimpleNorm variants that can be enabled to force computation of norm in float32
Some typing, argument cleanup for norm, norm+act layers done with above
Support Naver ROPE-ViT (https://github.com/naver-ai/rope-vit) in eva.py, add RotaryEmbeddingMixed module for mixed mode, weights on HuggingFace Hub

model	img_size	top1	top5	param_count
vit_large_patch16_rope_mixed_ape_224.naver_in1k	224	84.84	97.122	304.4
vit_large_patch16_rope_mixed_224.naver_in1k	224	84.828	97.116	304.2
vit_large_patch16_rope_ape_224.naver_in1k	224	84.65	97.154	304.37
vit_large_patch16_rope_224.naver_in1k	224	84.648	97.122	304.17
vit_base_patch16_rope_mixed_ape_224.naver_in1k	224	83.894	96.754	86.59
vit_base_patch16_rope_mixed_224.naver_in1k	224	83.804	96.712	86.44
vit_base_patch16_rope_ape_224.naver_in1k	224	83.782	96.61	86.59
vit_base_patch16_rope_224.naver_in1k	224	83.718	96.672	86.43
vit_small_patch16_rope_224.naver_in1k	224	81.23	95.022	21.98
vit_small_patch16_rope_mixed_224.naver_in1k	224	81.216	95.022	21.99
vit_small_patch16_rope_ape_224.naver_in1k	224	81.004	95.016	22.06
vit_small_patch16_rope_mixed_ape_224.naver_in1k	224	80.986	94.976	22.06

Some cleanup of ROPE modules, helpers, and FX tracing leaf registration
Preparing version 1.0.17 release

June 26, 2025

MobileNetV5 backbone (w/ encoder only variant) for Gemma 3n image encoder
Version 1.0.16 released

June 23, 2025

Add F.grid_sample based 2D and factorized pos embed resize to NaFlexViT. Faster when lots of different sizes (based on example by https://github.com/stas-sl).
Further speed up patch embed resample by replacing vmap with matmul (based on snippet by https://github.com/stas-sl).
Add 3 initial native aspect NaFlexViT checkpoints created while testing, ImageNet-1k and 3 different pos embed configs w/ same hparams.

Model	Top-1 Acc	Top-5 Acc	Params (M)	Eval Seq Len
naflexvit_base_patch16_par_gap.e300_s576_in1k	83.67	96.45	86.63	576
naflexvit_base_patch16_parfac_gap.e300_s576_in1k	83.63	96.41	86.46	576
naflexvit_base_patch16_gap.e300_s576_in1k	83.50	96.46	86.63	576

Support gradient checkpointing for forward_intermediates and fix some checkpointing bugs. Thanks https://github.com/brianhou0208
Add 'corrected weight decay' (https://arxiv.org/abs/2506.02285) as option to AdamW (legacy), Adopt, Kron, Adafactor (BV), Lamb, LaProp, Lion, NadamW, RmsPropTF, SGDW optimizers
Switch PE (perception encoder) ViT models to use native timm weights instead of remapping on the fly
Fix cuda stream bug in prefetch loader

June 5, 2025

Initial NaFlexVit model code. NaFlexVit is a Vision Transformer with:
1. Encapsulated embedding and position encoding in a single module
2. Support for nn.Linear patch embedding on pre-patchified (dictionary) inputs
3. Support for NaFlex variable aspect, variable resolution (SigLip-2: https://arxiv.org/abs/2502.14786)
4. Support for FlexiViT variable patch size (https://arxiv.org/abs/2212.08013)
5. Support for NaViT fractional/factorized position embedding (https://arxiv.org/abs/2307.06304)
Existing vit models in vision_transformer.py can be loaded into the NaFlexVit model by adding the use_naflex=True flag to create_model
- Some native weights coming soon
A full NaFlex data pipeline is available that allows training / fine-tuning / evaluating with variable aspect / size images
- To enable in train.py and validate.py add the --naflex-loader arg, must be used with a NaFlexVit
To evaluate an existing (classic) ViT loaded in NaFlexVit model w/ NaFlex data pipe:
- python validate.py /imagenet --amp -j 8 --model vit_base_patch16_224 --model-kwargs use_naflex=True --naflex-loader --naflex-max-seq-len 256
The training has some extra args features worth noting
- The --naflex-train-seq-lens' argument specifies which sequence lengths to randomly pick from per batch during training
- The --naflex-max-seq-len argument sets the target sequence length for validation
- Adding --model-kwargs enable_patch_interpolator=True --naflex-patch-sizes 12 16 24 will enable random patch size selection per-batch w/ interpolation
- The --naflex-loss-scale arg changes loss scaling mode per batch relative to the batch size, timm NaFlex loading changes the batch size for each seq len

May 28, 2025

Add a number of small/fast models thanks to https://github.com/brianhou0208
- SwiftFormer - (ICCV2023) SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications
- FasterNet - (CVPR2023) Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks
- SHViT - (CVPR2024) SHViT: Single-Head Vision Transformer with Memory Efficient
- StarNet - (CVPR2024) Rewrite the Stars
- GhostNet-V3 GhostNetV3: Exploring the Training Strategies for Compact Models
Update EVA ViT (closest match) to support Perception Encoder models (https://arxiv.org/abs/2504.13181) from Meta, loading Hub weights but I still need to push dedicated timm weights
- Add some flexibility to ROPE impl
Big increase in number of models supporting forward_intermediates() and some additional fixes thanks to https://github.com/brianhou0208
- DaViT, EdgeNeXt, EfficientFormerV2, EfficientViT(MIT), EfficientViT(MSRA), FocalNet, GCViT, HGNet /V2, InceptionNeXt, Inception-V4, MambaOut, MetaFormer, NesT, Next-ViT, PiT, PVT V2, RepGhostNet, RepViT, ResNetV2, ReXNet, TinyViT, TResNet, VoV
TNT model updated w/ new weights forward_intermediates() thanks to https://github.com/brianhou0208
Add local-dir: pretrained schema, can use local-dir:/path/to/model/folder for model name to source model / pretrained cfg & weights Hugging Face Hub models (config.json + weights file) from a local folder.
Fixes, improvements for onnx export

Feb 21, 2025

SigLIP 2 ViT image encoders added (https://huggingface.co/collections/timm/siglip-2-67b8e72ba08b09dd97aecaf9)
- Variable resolution / aspect NaFlex versions are a WIP
Add 'SO150M2' ViT weights trained with SBB recipes, great results, better for ImageNet than previous attempt w/ less training.
- vit_so150m2_patch16_reg1_gap_448.sbb_e200_in12k_ft_in1k - 88.1% top-1
- vit_so150m2_patch16_reg1_gap_384.sbb_e200_in12k_ft_in1k - 87.9% top-1
- vit_so150m2_patch16_reg1_gap_256.sbb_e200_in12k_ft_in1k - 87.3% top-1
- vit_so150m2_patch16_reg4_gap_256.sbb_e200_in12k
Updated InternViT-300M '2.5' weights
Release 1.0.15

Feb 1, 2025

FYI PyTorch 2.6 & Python 3.13 are tested and working w/ current main and released version of timm

Jan 27, 2025

Add Kron Optimizer (PSGD w/ Kronecker-factored preconditioner)
- Code from https://github.com/evanatyourservice/kron_torch
- See also https://sites.google.com/site/lixilinx/home/psgd

Jan 19, 2025

Fix loading of LeViT safetensor weights, remove conversion code which should have been deactivated
Add 'SO150M' ViT weights trained with SBB recipes, decent results, but not optimal shape for ImageNet-12k/1k pretrain/ft
- vit_so150m_patch16_reg4_gap_256.sbb_e250_in12k_ft_in1k - 86.7% top-1
- vit_so150m_patch16_reg4_gap_384.sbb_e250_in12k_ft_in1k - 87.4% top-1
- vit_so150m_patch16_reg4_gap_256.sbb_e250_in12k
Misc typing, typo, etc. cleanup
1.0.14 release to get above LeViT fix out

Jan 9, 2025

Add support to train and validate in pure bfloat16 or float16
wandb project name arg added by https://github.com/caojiaolong, use arg.experiment for name
Fix old issue w/ checkpoint saving not working on filesystem w/o hard-link support (e.g. FUSE fs mounts)
1.0.13 release

Jan 6, 2025

Add torch.utils.checkpoint.checkpoint() wrapper in timm.models that defaults use_reentrant=False, unless TIMM_REENTRANT_CKPT=1 is set in env.

Dec 31, 2024

convnext_nano 384x384 ImageNet-12k pretrain & fine-tune. https://huggingface.co/models?search=convnext_nano%20r384
Add AIM-v2 encoders from https://github.com/apple/ml-aim, see on Hub: https://huggingface.co/models?search=timm%20aimv2
Add PaliGemma2 encoders from https://github.com/google-research/big_vision to existing PaliGemma, see on Hub: https://huggingface.co/models?search=timm%20pali2
Add missing L/14 DFN2B 39B CLIP ViT, vit_large_patch14_clip_224.dfn2b_s39b
Fix existing RmsNorm layer & fn to match standard formulation, use PT 2.5 impl when possible. Move old impl to SimpleNorm layer, it's LN w/o centering or bias. There were only two timm models using it, and they have been updated.
Allow override of cache_dir arg for model creation
Pass through trust_remote_code for HF datasets wrapper
inception_next_atto model added by creator
Adan optimizer caution, and Lamb decoupled weight decay options
Some feature_info metadata fixed by https://github.com/brianhou0208
All OpenCLIP and JAX (CLIP, SigLIP, Pali, etc) model weights that used load time remapping were given their own HF Hub instances so that they work with hf-hub: based loading, and thus will work with new Transformers TimmWrapperModel

Nov 28, 2024

More optimizers
- Add MARS optimizer (https://arxiv.org/abs/2411.10438, https://github.com/AGI-Arena/MARS)
- Add LaProp optimizer (https://arxiv.org/abs/2002.04839, https://github.com/Z-T-WANG/LaProp-Optimizer)
- Add masking from 'Cautious Optimizers' (https://arxiv.org/abs/2411.16085, https://github.com/kyleliang919/C-Optim) to Adafactor, Adafactor Big Vision, AdamW (legacy), Adopt, Lamb, LaProp, Lion, NadamW, RMSPropTF, SGDW
- Cleanup some docstrings and type annotations re optimizers and factory
Add MobileNet-V4 Conv Medium models pretrained on in12k and fine-tuned in1k @ 384x384
Add small cs3darknet, quite good for the speed
- https://huggingface.co/timm/cs3darknet_focus_s.ra4_e3600_r256_in1k

Nov 12, 2024

Optimizer factory refactor
- New factory works by registering optimizers using an OptimInfo dataclass w/ some key traits
- Add list_optimizers, get_optimizer_class, get_optimizer_info to reworked create_optimizer_v2 fn to explore optimizers, get info or class
- deprecate optim.optim_factory, move fns to optim/_optim_factory.py and optim/_param_groups.py and encourage import via timm.optim
Add Adopt (https://github.com/iShohei220/adopt) optimizer
Add 'Big Vision' variant of Adafactor (https://github.com/google-research/big_vision/blob/main/big_vision/optax.py) optimizer
Fix original Adafactor to pick better factorization dims for convolutions
Tweak LAMB optimizer with some improvements in torch.where functionality since original, refactor clipping a bit
dynamic img size support in vit, deit, eva improved to support resize from non-square patch grids, thanks https://github.com/wojtke

Oct 31, 2024

Add a set of new very well trained ResNet & ResNet-V2 18/34 (basic block) weights. See https://huggingface.co/blog/rwightman/resnet-trick-or-treat

Oct 19, 2024

Cleanup torch amp usage to avoid cuda specific calls, merge support for Ascend (NPU) devices from MengqingCao that should work now in PyTorch 2.5 w/ new device extension autoloading feature. Tested Intel Arc (XPU) in Pytorch 2.5 too and it (mostly) worked.

Oct 16, 2024

Fix error on importing from deprecated path timm.models.registry, increased priority of existing deprecation warnings to be visible
Port weights of InternViT-300M (https://huggingface.co/OpenGVLab/InternViT-300M-448px) to timm as vit_intern300m_patch14_448

Oct 14, 2024

Pre-activation (ResNetV2) version of 18/18d/34/34d ResNet model defs added by request (weights pending)
Release 1.0.10

Oct 11, 2024

MambaOut (https://github.com/yuweihao/MambaOut) model & weights added. A cheeky take on SSM vision models w/o the SSM (essentially ConvNeXt w/ gating). A mix of original weights + custom variations & weights.

model	img_size	top1	top5	param_count
mambaout_base_plus_rw.sw_e150_r384_in12k_ft_in1k	384	87.506	98.428	101.66
mambaout_base_plus_rw.sw_e150_in12k_ft_in1k	288	86.912	98.236	101.66
mambaout_base_plus_rw.sw_e150_in12k_ft_in1k	224	86.632	98.156	101.66
mambaout_base_tall_rw.sw_e500_in1k	288	84.974	97.332	86.48
mambaout_base_wide_rw.sw_e500_in1k	288	84.962	97.208	94.45
mambaout_base_short_rw.sw_e500_in1k	288	84.832	97.27	88.83
mambaout_base.in1k	288	84.72	96.93	84.81
mambaout_small_rw.sw_e450_in1k	288	84.598	97.098	48.5
mambaout_small.in1k	288	84.5	96.974	48.49
mambaout_base_wide_rw.sw_e500_in1k	224	84.454	96.864	94.45
mambaout_base_tall_rw.sw_e500_in1k	224	84.434	96.958	86.48
mambaout_base_short_rw.sw_e500_in1k	224	84.362	96.952	88.83
mambaout_base.in1k	224	84.168	96.68	84.81
mambaout_small.in1k	224	84.086	96.63	48.49
mambaout_small_rw.sw_e450_in1k	224	84.024	96.752	48.5
mambaout_tiny.in1k	288	83.448	96.538	26.55
mambaout_tiny.in1k	224	82.736	96.1	26.55
mambaout_kobe.in1k	288	81.054	95.718	9.14
mambaout_kobe.in1k	224	79.986	94.986	9.14
mambaout_femto.in1k	288	79.848	95.14	7.3
mambaout_femto.in1k	224	78.87	94.408	7.3

SigLIP SO400M ViT fine-tunes on ImageNet-1k @ 378x378, added 378x378 option for existing SigLIP 384x384 models
- vit_so400m_patch14_siglip_378.webli_ft_in1k - 89.42 top-1
- vit_so400m_patch14_siglip_gap_378.webli_ft_in1k - 89.03
SigLIP SO400M ViT encoder from recent multi-lingual (i18n) variant, patch16 @ 256x256 (https://huggingface.co/timm/ViT-SO400M-16-SigLIP-i18n-256). OpenCLIP update pending.
Add two ConvNeXt 'Zepto' models & weights (one w/ overlapped stem and one w/ patch stem). Uses RMSNorm, smaller than previous 'Atto', 2.2M params.
- convnext_zepto_rms_ols.ra4_e3600_r224_in1k - 73.20 top-1 @ 224
- convnext_zepto_rms.ra4_e3600_r224_in1k - 72.81 @ 224

Sept 2024

Add a suite of tiny test models for improved unit tests and niche low-resource applications (https://huggingface.co/blog/rwightman/timm-tiny-test)
Add MobileNetV4-Conv-Small (0.5x) model (https://huggingface.co/posts/rwightman/793053396198664)
- mobilenetv4_conv_small_050.e3000_r224_in1k - 65.81 top-1 @ 256, 64.76 @ 224
Add MobileNetV3-Large variants trained with MNV4 Small recipe
- mobilenetv3_large_150d.ra4_e3600_r256_in1k - 81.81 @ 320, 80.94 @ 256
- mobilenetv3_large_100.ra4_e3600_r224_in1k - 77.16 @ 256, 76.31 @ 224

Aug 21, 2024

Updated SBB ViT models trained on ImageNet-12k and fine-tuned on ImageNet-1k, challenging quite a number of much larger, slower models

model	top1	top5	param_count	img_size
vit_mediumd_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k	87.438	98.256	64.11	384
vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k	86.608	97.934	64.11	256
vit_betwixt_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k	86.594	98.02	60.4	384
vit_betwixt_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k	85.734	97.61	60.4	256

MobileNet-V1 1.25, EfficientNet-B1, & ResNet50-D weights w/ MNV4 baseline challenge recipe

model	top1	top5	param_count	img_size
resnet50d.ra4_e3600_r224_in1k	81.838	95.922	25.58	288
efficientnet_b1.ra4_e3600_r240_in1k	81.440	95.700	7.79	288
resnet50d.ra4_e3600_r224_in1k	80.952	95.384	25.58	224
efficientnet_b1.ra4_e3600_r240_in1k	80.406	95.152	7.79	240
mobilenetv1_125.ra4_e3600_r224_in1k	77.600	93.804	6.27	256
mobilenetv1_125.ra4_e3600_r224_in1k	76.924	93.234	6.27	224

Add SAM2 (HieraDet) backbone arch & weight loading support
Add Hiera Small weights trained w/ abswin pos embed on in12k & fine-tuned on 1k

model	top1	top5	param_count
hiera_small_abswin_256.sbb2_e200_in12k_ft_in1k	84.912	97.260	35.01
hiera_small_abswin_256.sbb2_pd_e200_in12k_ft_in1k	84.560	97.106	35.01

Aug 8, 2024

Add RDNet ('DenseNets Reloaded', https://arxiv.org/abs/2403.19588), thanks Donghyun Kim

July 28, 2024

Add mobilenet_edgetpu_v2_m weights w/ ra4 mnv4-small based recipe. 80.1% top-1 @ 224 and 80.7 @ 256.
Release 1.0.8

July 26, 2024

More MobileNet-v4 weights, ImageNet-12k pretrain w/ fine-tunes, and anti-aliased ConvLarge models

model	top1	top1_err	top5	top5_err	param_count	img_size
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k	84.99	15.01	97.294	2.706	32.59	544
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k	84.772	15.228	97.344	2.656	32.59	480
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k	84.64	15.36	97.114	2.886	32.59	448
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k	84.314	15.686	97.102	2.898	32.59	384
mobilenetv4_conv_aa_large.e600_r384_in1k	83.824	16.176	96.734	3.266	32.59	480
mobilenetv4_conv_aa_large.e600_r384_in1k	83.244	16.756	96.392	3.608	32.59	384
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k	82.99	17.01	96.67	3.33	11.07	320
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k	82.364	17.636	96.256	3.744	11.07	256

Impressive MobileNet-V1 and EfficientNet-B0 baseline challenges (https://huggingface.co/blog/rwightman/mobilenet-baselines)

model	top1	top1_err	top5	top5_err	param_count	img_size
efficientnet_b0.ra4_e3600_r224_in1k	79.364	20.636	94.754	5.246	5.29	256
efficientnet_b0.ra4_e3600_r224_in1k	78.584	21.416	94.338	5.662	5.29	224
mobilenetv1_100h.ra4_e3600_r224_in1k	76.596	23.404	93.272	6.728	5.28	256
mobilenetv1_100.ra4_e3600_r224_in1k	76.094	23.906	93.004	6.996	4.23	256
mobilenetv1_100h.ra4_e3600_r224_in1k	75.662	24.338	92.504	7.496	5.28	224
mobilenetv1_100.ra4_e3600_r224_in1k	75.382	24.618	92.312	7.688	4.23	224

Prototype of set_input_size() added to vit and swin v1/v2 models to allow changing image size, patch size, window size after model creation.
Improved support in swin for different size handling, in addition to set_input_size, always_partition and strict_img_size args have been added to __init__ to allow more flexible input size constraints
Fix out of order indices info for intermediate 'Getter' feature wrapper, check out or range indices for same.
Add several tiny < .5M param models for testing that are actually trained on ImageNet-1k

model	top1	top1_err	top5	top5_err	param_count	img_size	crop_pct
test_efficientnet.r160_in1k	47.156	52.844	71.726	28.274	0.36	192	1.0
test_byobnet.r160_in1k	46.698	53.302	71.674	28.326	0.46	192	1.0
test_efficientnet.r160_in1k	46.426	53.574	70.928	29.072	0.36	160	0.875
test_byobnet.r160_in1k	45.378	54.622	70.572	29.428	0.46	160	0.875
test_vit.r160_in1k	42.0	58.0	68.664	31.336	0.37	192	1.0
test_vit.r160_in1k	40.822	59.178	67.212	32.788	0.37	160	0.875

Fix vit reg token init, thanks Promisery
Other misc fixes

June 24, 2024

3 more MobileNetV4 hybrid weights with different MQA weight init scheme

model	top1	top1_err	top5	top5_err	param_count	img_size
mobilenetv4_hybrid_large.ix_e600_r384_in1k	84.356	15.644	96.892	3.108	37.76	448
mobilenetv4_hybrid_large.ix_e600_r384_in1k	83.990	16.010	96.702	3.298	37.76	384
mobilenetv4_hybrid_medium.ix_e550_r384_in1k	83.394	16.606	96.760	3.240	11.07	448
mobilenetv4_hybrid_medium.ix_e550_r384_in1k	82.968	17.032	96.474	3.526	11.07	384
mobilenetv4_hybrid_medium.ix_e550_r256_in1k	82.492	17.508	96.278	3.722	11.07	320
mobilenetv4_hybrid_medium.ix_e550_r256_in1k	81.446	18.554	95.704	4.296	11.07	256

florence2 weight loading in DaViT model

June 12, 2024

MobileNetV4 models and initial set of timm trained weights added:

model	top1	top1_err	top5	top5_err	param_count	img_size
mobilenetv4_hybrid_large.e600_r384_in1k	84.266	15.734	96.936	3.064	37.76	448
mobilenetv4_hybrid_large.e600_r384_in1k	83.800	16.200	96.770	3.230	37.76	384
mobilenetv4_conv_large.e600_r384_in1k	83.392	16.608	96.622	3.378	32.59	448
mobilenetv4_conv_large.e600_r384_in1k	82.952	17.048	96.266	3.734	32.59	384
mobilenetv4_conv_large.e500_r256_in1k	82.674	17.326	96.31	3.69	32.59	320
mobilenetv4_conv_large.e500_r256_in1k	81.862	18.138	95.69	4.31	32.59	256
mobilenetv4_hybrid_medium.e500_r224_in1k	81.276	18.724	95.742	4.258	11.07	256
mobilenetv4_conv_medium.e500_r256_in1k	80.858	19.142	95.768	4.232	9.72	320
mobilenetv4_hybrid_medium.e500_r224_in1k	80.442	19.558	95.38	4.62	11.07	224
mobilenetv4_conv_blur_medium.e500_r224_in1k	80.142	19.858	95.298	4.702	9.72	256
mobilenetv4_conv_medium.e500_r256_in1k	79.928	20.072	95.184	4.816	9.72	256
mobilenetv4_conv_medium.e500_r224_in1k	79.808	20.192	95.186	4.814	9.72	256
mobilenetv4_conv_blur_medium.e500_r224_in1k	79.438	20.562	94.932	5.068	9.72	224
mobilenetv4_conv_medium.e500_r224_in1k	79.094	20.906	94.77	5.23	9.72	224
mobilenetv4_conv_small.e2400_r224_in1k	74.616	25.384	92.072	7.928	3.77	256
mobilenetv4_conv_small.e1200_r224_in1k	74.292	25.708	92.116	7.884	3.77	256
mobilenetv4_conv_small.e2400_r224_in1k	73.756	26.244	91.422	8.578	3.77	224
mobilenetv4_conv_small.e1200_r224_in1k	73.454	26.546	91.34	8.66	3.77	224

Apple MobileCLIP (https://arxiv.org/pdf/2311.17049, FastViT and ViT-B) image tower model support & weights added (part of OpenCLIP support).
ViTamin (https://arxiv.org/abs/2404.02132) CLIP image tower model & weights added (part of OpenCLIP support).
OpenAI CLIP Modified ResNet image tower modelling & weight support (via ByobNet). Refactor AttentionPool2d.

May 14, 2024

Support loading PaliGemma jax weights into SigLIP ViT models with average pooling.
Add Hiera models from Meta (https://github.com/facebookresearch/hiera).
Add normalize= flag for transforms, return non-normalized torch.Tensor with original dtype (for chug)
Version 1.0.3 release

May 11, 2024

Searching for Better ViT Baselines (For the GPU Poor) weights and vit variants released. Exploring model shapes between Tiny and Base.

model	top1	top5	param_count	img_size
vit_mediumd_patch16_reg4_gap_256.sbb_in12k_ft_in1k	86.202	97.874	64.11	256
vit_betwixt_patch16_reg4_gap_256.sbb_in12k_ft_in1k	85.418	97.48	60.4	256
vit_mediumd_patch16_rope_reg1_gap_256.sbb_in1k	84.322	96.812	63.95	256
vit_betwixt_patch16_rope_reg4_gap_256.sbb_in1k	83.906	96.684	60.23	256
vit_base_patch16_rope_reg1_gap_256.sbb_in1k	83.866	96.67	86.43	256
vit_medium_patch16_rope_reg1_gap_256.sbb_in1k	83.81	96.824	38.74	256
vit_betwixt_patch16_reg4_gap_256.sbb_in1k	83.706	96.616	60.4	256
vit_betwixt_patch16_reg1_gap_256.sbb_in1k	83.628	96.544	60.4	256
vit_medium_patch16_reg4_gap_256.sbb_in1k	83.47	96.622	38.88	256
vit_medium_patch16_reg1_gap_256.sbb_in1k	83.462	96.548	38.88	256
vit_little_patch16_reg4_gap_256.sbb_in1k	82.514	96.262	22.52	256
vit_wee_patch16_reg1_gap_256.sbb_in1k	80.256	95.360	13.42	256
vit_pwee_patch16_reg1_gap_256.sbb_in1k	80.072	95.136	15.25	256
vit_mediumd_patch16_reg4_gap_256.sbb_in12k	N/A	N/A	64.11	256
vit_betwixt_patch16_reg4_gap_256.sbb_in12k	N/A	N/A	60.4	256

AttentionExtract helper added to extract attention maps from timm models. See example in https://github.com/huggingface/pytorch-image-models/discussions/1232#discussioncomment-9320949
forward_intermediates() API refined and added to more models including some ConvNets that have other extraction methods.
1017 of 1047 model architectures support features_only=True feature extraction. Remaining 34 architectures can be supported but based on priority requests.
Remove torch.jit.script annotated functions including old JIT activations. Conflict with dynamo and dynamo does a much better job when used.

April 11, 2024

Prepping for a long overdue 1.0 release, things have been stable for a while now.
Significant feature that's been missing for a while, features_only=True support for ViT models with flat hidden states or non-std module layouts (so far covering 'vit_*', 'twins_*', 'deit*', 'beit*', 'mvitv2*', 'eva*', 'samvit_*', 'flexivit*')
Above feature support achieved through a new forward_intermediates() API that can be used with a feature wrapping module or directly.

model = timm.create_model('vit_base_patch16_224')
final_feat, intermediates = model.forward_intermediates(input) 
output = model.forward_head(final_feat)  # pooling + classifier head

print(final_feat.shape)
torch.Size([2, 197, 768])

for f in intermediates:
    print(f.shape)
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])

print(output.shape)
torch.Size([2, 1000])

model = timm.create_model('eva02_base_patch16_clip_224', pretrained=True, img_size=512, features_only=True, out_indices=(-3, -2,))
output = model(torch.randn(2, 3, 512, 512))

for o in output:    
    print(o.shape)   
torch.Size([2, 768, 32, 32])
torch.Size([2, 768, 32, 32])

TinyCLIP vision tower weights added, thx Thien Tran

Feb 19, 2024

Next-ViT models added. Adapted from https://github.com/bytedance/Next-ViT
HGNet and PP-HGNetV2 models added. Adapted from https://github.com/PaddlePaddle/PaddleClas by SeeFun
Removed setup.py, moved to pyproject.toml based build supported by PDM
Add updated model EMA impl using _for_each for less overhead
Support device args in train script for non GPU devices
Other misc fixes and small additions
Min supported Python version increased to 3.8
Release 0.9.16

Introduction

PyTorch Image Models (timm) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results.

The work of many others is present here. I've tried to make sure all source material is acknowledged via links to github, arxiv papers, etc in the README, documentation, and code docstrings. Please let me know if I missed anything.

Features

Models

All model architecture families include variants with pretrained weights. There are specific model variants without any weights, it is NOT a bug. Help training new or better weights is always appreciated.

Aggregating Nested Transformers - https://arxiv.org/abs/2105.12723
BEiT - https://arxiv.org/abs/2106.08254
BEiT-V2 - https://arxiv.org/abs/2208.06366
BEiT3 - https://arxiv.org/abs/2208.10442
Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
Bottleneck Transformers - https://arxiv.org/abs/2101.11605
CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239
CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399
CoAtNet (Convolution and Attention) - https://arxiv.org/abs/2106.04803
ConvNeXt - https://arxiv.org/abs/2201.03545
ConvNeXt-V2 - http://arxiv.org/abs/2301.00808
ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929
DeiT - https://arxiv.org/abs/2012.12877
DeiT-III - https://arxiv.org/pdf/2204.07118.pdf
DenseNet - https://arxiv.org/abs/1608.06993
DLA - https://arxiv.org/abs/1707.06484
DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629
EdgeNeXt - https://arxiv.org/abs/2206.10589
EfficientFormer - https://arxiv.org/abs/2206.01191
EfficientFormer-V2 - https://arxiv.org/abs/2212.08059
EfficientNet (MBConvNet Family)
- EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252
- EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665
- EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946
- EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html
- EfficientNet V2 - https://arxiv.org/abs/2104.00298
- FBNet-C - https://arxiv.org/abs/1812.03443
- MixNet - https://arxiv.org/abs/1907.09595
- MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626
- MobileNet-V2 - https://arxiv.org/abs/1801.04381
- Single-Path NAS - https://arxiv.org/abs/1904.02877
- TinyNet - https://arxiv.org/abs/2010.14819
EfficientViT (MIT) - https://arxiv.org/abs/2205.14756
EfficientViT (MSRA) - https://arxiv.org/abs/2305.07027
EVA - https://arxiv.org/abs/2211.07636
EVA-02 - https://arxiv.org/abs/2303.11331
FasterNet - https://arxiv.org/abs/2303.03667
FastViT - https://arxiv.org/abs/2303.14189
FlexiViT - https://arxiv.org/abs/2212.08013
FocalNet (Focal Modulation Networks) - https://arxiv.org/abs/2203.11926
GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959
GhostNet - https://arxiv.org/abs/1911.11907
GhostNet-V2 - https://arxiv.org/abs/2211.12905
GhostNet-V3 - https://arxiv.org/abs/2404.11202
gMLP - https://arxiv.org/abs/2105.08050
GPU-Efficient Networks - https://arxiv.org/abs/2006.14090
Halo Nets - https://arxiv.org/abs/2103.12731
HGNet / HGNet-V2 - TBD
HRNet - https://arxiv.org/abs/1908.07919
InceptionNeXt - https://arxiv.org/abs/2303.16900
Inception-V3 - https://arxiv.org/abs/1512.00567
Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
Lambda Networks - https://arxiv.org/abs/2102.08602
LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136
MambaOut - https://arxiv.org/abs/2405.07992
MaxViT (Multi-Axis Vision Transformer) - https://arxiv.org/abs/2204.01697
MetaFormer (PoolFormer-v2, ConvFormer, CAFormer) - https://arxiv.org/abs/2210.13452
MLP-Mixer - https://arxiv.org/abs/2105.01601
MobileCLIP - https://arxiv.org/abs/2311.17049
MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244
- FBNet-V3 - https://arxiv.org/abs/2006.02049
- HardCoRe-NAS - https://arxiv.org/abs/2102.11646
- LCNet - https://arxiv.org/abs/2109.15099
MobileNetV4 - https://arxiv.org/abs/2404.10518
MobileOne - https://arxiv.org/abs/2206.04040
MobileViT - https://arxiv.org/abs/2110.02178
MobileViT-V2 - https://arxiv.org/abs/2206.02680
MViT-V2 (Improved Multiscale Vision Transformer) - https://arxiv.org/abs/2112.01526
NASNet-A - https://arxiv.org/abs/1707.07012
NesT - https://arxiv.org/abs/2105.12723
Next-ViT - https://arxiv.org/abs/2207.05501
NFNet-F - https://arxiv.org/abs/2102.06171
NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692
PNasNet - https://arxiv.org/abs/1712.00559
PoolFormer (MetaFormer) - https://arxiv.org/abs/2111.11418
Pooling-based Vision Transformer (PiT) - https://arxiv.org/abs/2103.16302
PVT-V2 (Improved Pyramid Vision Transformer) - https://arxiv.org/abs/2106.13797
RDNet (DenseNets Reloaded) - https://arxiv.org/abs/2403.19588
RegNet - https://arxiv.org/abs/2003.13678
RegNetZ - https://arxiv.org/abs/2103.06877
RepVGG - https://arxiv.org/abs/2101.03697
RepGhostNet - https://arxiv.org/abs/2211.06088
RepViT - https://arxiv.org/abs/2307.09283
ResMLP - https://arxiv.org/abs/2105.03404
ResNet/ResNeXt
- ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385
- ResNeXt - https://arxiv.org/abs/1611.05431
- 'Bag of Tricks' / Gluon C, D, E, S variations - https://arxiv.org/abs/1812.01187
- Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 - https://arxiv.org/abs/1805.00932
- Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts - https://arxiv.org/abs/1905.00546
- ECA-Net (ECAResNet) - https://arxiv.org/abs/1910.03151v4
- Squeeze-and-Excitation Networks (SEResNet) - https://arxiv.org/abs/1709.01507
- ResNet-RS - https://arxiv.org/abs/2103.07579
Res2Net - https://arxiv.org/abs/1904.01169
ResNeSt - https://arxiv.org/abs/2004.08955
ReXNet - https://arxiv.org/abs/2007.00992
ROPE-ViT - https://arxiv.org/abs/2403.13298
SelecSLS - https://arxiv.org/abs/1907.00837
Selective Kernel Networks - https://arxiv.org/abs/1903.06586
Sequencer2D - https://arxiv.org/abs/2205.01972
SHViT - https://arxiv.org/abs/2401.16456
SigLIP (image encoder) - https://arxiv.org/abs/2303.15343
SigLIP 2 (image encoder) - https://arxiv.org/abs/2502.14786
StarNet - https://arxiv.org/abs/2403.19967
SwiftFormer - https://arxiv.org/pdf/2303.15446
Swin S3 (AutoFormerV2) - https://arxiv.org/abs/2111.14725
Swin Transformer - https://arxiv.org/abs/2103.14030
Swin Transformer V2 - https://arxiv.org/abs/2111.09883
TinyViT - https://arxiv.org/abs/2207.10666
Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112
TResNet - https://arxiv.org/abs/2003.13630
Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/pdf/2104.13840.pdf
VGG - https://arxiv.org/abs/1409.1556
Visformer - https://arxiv.org/abs/2104.12533
Vision Transformer - https://arxiv.org/abs/2010.11929
ViTamin - https://arxiv.org/abs/2404.02132
VOLO (Vision Outlooker) - https://arxiv.org/abs/2106.13112
VovNet V2 and V1 - https://arxiv.org/abs/1911.06667
Xception - https://arxiv.org/abs/1610.02357
Xception (Modified Aligned, Gluon) - https://arxiv.org/abs/1802.02611
Xception (Modified Aligned, TF) - https://arxiv.org/abs/1802.02611
XCiT (Cross-Covariance Image Transformers) - https://arxiv.org/abs/2106.09681

Optimizers

To see full list of optimizers w/ descriptions: timm.optim.list_optimizers(with_description=True)

Included optimizers available via timm.optim.create_optimizer_v2 factory method:

adabelief an implementation of AdaBelief adapted from https://github.com/juntang-zhuang/Adabelief-Optimizer - https://arxiv.org/abs/2010.07468
adafactor adapted from FAIRSeq impl - https://arxiv.org/abs/1804.04235
adafactorbv adapted from Big Vision - https://arxiv.org/abs/2106.04560
adahessian by David Samuel - https://arxiv.org/abs/2006.00719
adamp and sgdp by Naver ClovAI - https://arxiv.org/abs/2006.08217
adan an implementation of Adan adapted from https://github.com/sail-sg/Adan - https://arxiv.org/abs/2208.06677
adopt ADOPT adapted from https://github.com/iShohei220/adopt - https://arxiv.org/abs/2411.02853
kron PSGD w/ Kronecker-factored preconditioner from https://github.com/evanatyourservice/kron_torch - https://sites.google.com/site/lixilinx/home/psgd
lamb an implementation of Lamb and LambC (w/ trust-clipping) cleaned up and modified to support use with XLA - https://arxiv.org/abs/1904.00962
laprop optimizer from https://github.com/Z-T-WANG/LaProp-Optimizer - https://arxiv.org/abs/2002.04839
lars an implementation of LARS and LARC (w/ trust-clipping) - https://arxiv.org/abs/1708.03888
lion and implementation of Lion adapted from https://github.com/google/automl/tree/master/lion - https://arxiv.org/abs/2302.06675
lookahead adapted from impl by Liam - https://arxiv.org/abs/1907.08610
madgrad an implementation of MADGRAD adapted from https://github.com/facebookresearch/madgrad - https://arxiv.org/abs/2101.11075
mars MARS optimizer from https://github.com/AGI-Arena/MARS - https://arxiv.org/abs/2411.10438
nadam an implementation of Adam w/ Nesterov momentum
nadamw an implementation of AdamW (Adam w/ decoupled weight-decay) w/ Nesterov momentum. A simplified impl based on https://github.com/mlcommons/algorithmic-efficiency
novograd by Masashi Kimura - https://arxiv.org/abs/1905.11286
radam by Liyuan Liu - https://arxiv.org/abs/1908.03265
rmsprop_tf adapted from PyTorch RMSProp by myself. Reproduces much improved Tensorflow RMSProp behaviour
sgdw and implementation of SGD w/ decoupled weight-decay
fused<name> optimizers by name with NVIDIA Apex installed
bnb<name> optimizers by name with BitsAndBytes installed
cadamw, clion, and more 'Cautious' optimizers from https://github.com/kyleliang919/C-Optim - https://arxiv.org/abs/2411.16085
adam, adamw, rmsprop, adadelta, adagrad, and sgd pass through to torch.optim implementations
c suffix (eg adamc, nadamc to implement 'corrected weight decay' in https://arxiv.org/abs/2506.02285)

Augmentations

Random Erasing from Zhun Zhong - https://arxiv.org/abs/1708.04896)
Mixup - https://arxiv.org/abs/1710.09412
CutMix - https://arxiv.org/abs/1905.04899
AutoAugment (https://arxiv.org/abs/1805.09501) and RandAugment (https://arxiv.org/abs/1909.13719) ImageNet configurations modeled after impl for EfficientNet training (https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py)
AugMix w/ JSD loss, JSD w/ clean + augmented mixing support works with AutoAugment and RandAugment as well - https://arxiv.org/abs/1912.02781
SplitBachNorm - allows splitting batch norm layers between clean and augmented (auxiliary batch norm) data

Regularization

DropPath aka "Stochastic Depth" - https://arxiv.org/abs/1603.09382
DropBlock - https://arxiv.org/abs/1810.12890
Blur Pooling - https://arxiv.org/abs/1904.11486

Other

Several (less common) features that I often utilize in my projects are included. Many of their additions are the reason why I maintain my own set of models, instead of using others' via PIP:

All models have a common default configuration interface and API for
- accessing/changing the classifier - get_classifier and reset_classifier
- doing a forward pass on just the features - forward_features (see documentation)
- these makes it easy to write consistent network wrappers that work with any of the models
All models support multi-scale feature map extraction (feature pyramids) via create_model (see documentation)
- create_model(name, features_only=True, out_indices=..., output_stride=...)
- out_indices creation arg specifies which feature maps to return, these indices are 0 based and generally correspond to the C(i + 1) feature level.
- output_stride creation arg controls output stride of the network by using dilated convolutions. Most networks are stride 32 by default. Not all networks support this.
- feature map channel counts, reduction level (stride) can be queried AFTER model creation via the .feature_info member
All models have a consistent pretrained weight loader that adapts last linear if necessary, and from 3 to 1 channel input if desired
High performance reference training, validation, and inference scripts that work in several process/GPU modes:
- NVIDIA DDP w/ a single GPU per process, multiple processes with APEX present (AMP mixed-precision optional)
- PyTorch DistributedDataParallel w/ multi-gpu, single process (AMP disabled as it crashes when enabled)
- PyTorch w/ single GPU single process (AMP optional)
A dynamic global pool implementation that allows selecting from average pooling, max pooling, average + max, or concat([average, max]) at model creation. All global pooling is adaptive average by default and compatible with pretrained weights.
A 'Test Time Pool' wrapper that can wrap any of the included models and usually provides improved performance doing inference with input images larger than the training size. Idea adapted from original DPN implementation when I ported (https://github.com/cypw/DPNs)
Learning rate schedulers
- Ideas adopted from
  - AllenNLP schedulers
  - FAIRseq lr_scheduler
  - SGDR: Stochastic Gradient Descent with Warm Restarts (https://arxiv.org/abs/1608.03983)
- Schedulers include step, cosine w/ restarts, tanh w/ restarts, plateau
Space-to-Depth by mrT23 (https://arxiv.org/abs/1801.04590) -- original paper?
Adaptive Gradient Clipping (https://arxiv.org/abs/2102.06171, https://github.com/deepmind/deepmind-research/tree/master/nfnets)
An extensive selection of channel and/or spatial attention modules:
- Bottleneck Transformer - https://arxiv.org/abs/2101.11605
- CBAM - https://arxiv.org/abs/1807.06521
- Effective Squeeze-Excitation (ESE) - https://arxiv.org/abs/1911.06667
- Efficient Channel Attention (ECA) - https://arxiv.org/abs/1910.03151
- Gather-Excite (GE) - https://arxiv.org/abs/1810.12348
- Global Context (GC) - https://arxiv.org/abs/1904.11492
- Halo - https://arxiv.org/abs/2103.12731
- Involution - https://arxiv.org/abs/2103.06255
- Lambda Layer - https://arxiv.org/abs/2102.08602
- Non-Local (NL) - https://arxiv.org/abs/1711.07971
- Squeeze-and-Excitation (SE) - https://arxiv.org/abs/1709.01507
- Selective Kernel (SK) - (https://arxiv.org/abs/1903.06586
- Split (SPLAT) - https://arxiv.org/abs/2004.08955
- Shifted Window (SWIN) - https://arxiv.org/abs/2103.14030

Results

Model validation results can be found in the results tables

Getting Started (Documentation)

The official documentation can be found at https://huggingface.co/docs/hub/timm. Documentation contributions are welcome.

Getting Started with PyTorch Image Models (timm): A Practitioner’s Guide by Chris Hughes is an extensive blog post covering many aspects of timm in detail.

timmdocs is an alternate set of documentation for timm. A big thanks to Aman Arora for his efforts creating timmdocs.

paperswithcode is a good resource for browsing the models within timm.

Train, Validation, Inference Scripts

The root folder of the repository contains reference train, validation, and inference scripts that work with the included models and other features of this repository. They are adaptable for other datasets and use cases with a little hacking. See documentation.

Awesome PyTorch Resources

One of the greatest assets of PyTorch is the community and their contributions. A few of my favourite resources that pair well with the models and components here are listed below.

Licenses

Code

The code here is licensed Apache 2.0. I've taken care to make sure any third party code included or adapted has compatible (permissive) licenses such as MIT, BSD, etc. I've made an effort to avoid any GPL / LGPL conflicts. That said, it is your responsibility to ensure you comply with licenses here and conditions of any dependent licenses. Where applicable, I've linked the sources/references for various components in docstrings. If you think I've missed anything please create an issue.

Pretrained Weights

So far all of the pretrained weights available here are pretrained on ImageNet with a select few that have some additional pretraining (see extra note below). ImageNet was released for non-commercial research purposes only (https://image-net.org/download). It's not clear what the implications of that are for the use of pretrained weights from that dataset. Any models I have trained with ImageNet are done for research purposes and one should assume that the original dataset license applies to the weights. It's best to seek legal advice if you intend to use the pretrained weights in a commercial product.

Pretrained on more than ImageNet

Several weights included or references here were pretrained with proprietary datasets that I do not have access to. These include the Facebook WSL, SSL, SWSL ResNe(Xt) and the Google Noisy Student EfficientNet models. The Facebook models have an explicit non-commercial license (CC-BY-NC 4.0, https://github.com/facebookresearch/semi-supervised-ImageNet1K-models, https://github.com/facebookresearch/WSL-Images). The Google models do not appear to have any restriction beyond the Apache 2.0 license (and ImageNet concerns). In either case, you should contact Facebook or Google with any questions.

Citing

BibTeX

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}

Latest DOI

Dependency	Optional
huggingface_hub	false
pyyaml	false
safetensors	false
torch	false
torchvision	false

Package profile

timm

Releases

Release table

PyPI Downloads

Dependencies

Details

Project URLs

Dependencies

Package description