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Hugging Face/timm (pytorch-image-models)

timm (pytorch-image-models)

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Releases3Avg0/wkVersionsv1.0.23 → v1.0.25
Mar 23, 2026
Release v1.0.26

March 23, 2026

  • Improve pickle checkpoint handling security. Default all loading to weights_only=True, add safe_global for ArgParse.
  • Improve attention mask handling for core ViT/EVA models & layers. Resolve bool masks, pass is_causal through for SSL tasks.
  • Fix class & register token uses with ViT and no pos embed enabled.
  • Add Patch Representation Refinement (PRR) as a pooling option in ViT. Thanks Sina (https://github.com/sinahmr).
  • Improve consistency of output projection / MLP dimensions for attention pooling layers.
  • Hiera model F.SDPA optimization to allow Flash Attention kernel use.
  • Caution added to SGDP optimizer.
  • Release 1.0.26. First maintenance release since my departure from Hugging Face.

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.25...v1.0.26

Feb 23, 2026
Release v1.0.25

Feb 23, 2026

  • Add token distillation training support to distillation task wrappers
  • Remove some torch.jit usage in prep for official deprecation
  • Caution added to AdamP optimizer
  • Call reset_parameters() even if meta-device init so that buffers get init w/ hacks like init_empty_weights
  • Tweak Muon optimizer to work with DTensor/FSDP2 (clamp_ instead of clamp_min_, alternate NS branch for DTensor)
  • Release 1.0.25

Jan 21, 2026

  • Compat Break: Fix oversight w/ QKV vs MLP bias in ParallelScalingBlock (& DiffParallelScalingBlock)
    • Does not impact any trained timm models but could impact downstream use.

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.24...v1.0.25

Jan 7, 2026
Release v1.0.24

Jan 5 & 6, 2025

  • Patch Release 1.0.24 (fix for 1.0.23)
  • Add new benchmark result csv files for inference timing on all models w/ RTX Pro 6000, 5090, and 4090 cards w/ PyTorch 2.9.1
  • Fix moved module error in deprecated timm.models.layers import path that impacts legacy imports
  • Release 1.0.23

Dec 30, 2025

Dec 12, 2025

Dec 1, 2025

  • Add lightweight task abstraction, add logits and feature distillation support to train script via new tasks.
  • Remove old APEX AMP support

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.22...v1.0.24

Jan 5, 2026
Release v1.0.23

Dec 30, 2025

Dec 12, 2025

Dec 1, 2025

  • Add lightweight task abstraction, add logits and feature distillation support to train script via new tasks.
  • Remove old APEX AMP support

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.22...v1.0.23

Nov 5, 2025
Release v1.0.22

Patch release for priority LayerScale initialization regression in 1.0.21

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.21...v1.0.22

Oct 24, 2025
Release v1.0.21

Oct 16-20, 2025

  • Add an impl of the Muon optimizer (based on https://github.com/KellerJordan/Muon) with customizations
    • extra flexibility and improved handling for conv weights and fallbacks for weight shapes not suited for orthogonalization
    • small speedup for NS iterations by reducing allocs and using fused (b)add(b)mm ops
    • by default uses AdamW (or NAdamW if nesterov=True) updates if muon not suitable for parameter shape (or excluded via param group flag)
    • like torch impl, select from several LR scale adjustment fns via adjust_lr_fn
    • select from several NS coefficient presets or specify your own via ns_coefficients
  • First 2 steps of 'meta' device model initialization supported
    • Fix several ops that were breaking creation under 'meta' device context
    • Add device & dtype factory kwarg support to all models and modules (anything inherting from nn.Module) in timm
  • License fields added to pretrained cfgs in code
  • Release 1.0.21

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.20...v1.0.21

Sep 21, 2025
Release v1.0.20

Sept 21, 2025

  • Remap DINOv3 ViT weight tags from lvd_1689m -> lvd1689m to match (same for sat_493m -> sat493m)
  • Release 1.0.20

Sept 17, 2025

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.19...v1.0.20

Jul 24, 2025
Release v1.0.19

Patch release for Python 3.9 compat break in 1.0.18

July 23, 2025

  • Add set_input_size() method to EVA models, used by OpenCLIP 3.0.0 to allow resizing for timm based encoder models.
  • Release 1.0.18, needed for PE-Core S & T models in OpenCLIP 3.0.0
  • Fix small typing issue that broke Python 3.9 compat. 1.0.19 patch release.

July 21, 2025

  • ROPE support added to NaFlexViT. All models covered by the EVA base (eva.py) including EVA, EVA02, Meta PE ViT, timm SBB ViT w/ ROPE, and Naver ROPE-ViT can be now loaded in NaFlexViT when use_naflex=True passed at model creation time
  • More Meta PE ViT encoders added, including small/tiny variants, lang variants w/ tiling, and more spatial variants.
  • PatchDropout fixed with NaFlexViT and also w/ EVA models (regression after adding Naver ROPE-ViT)
  • Fix XY order with grid_indexing='xy', impacted non-square image use in 'xy' mode (only ROPE-ViT and PE impacted).

What's Changed

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.17...v1.0.18

Jul 23, 2025
Release v1.0.18

July 23, 2025

  • Add set_input_size() method to EVA models, used by OpenCLIP 3.0.0 to allow resizing for timm based encoder models.
  • Release 1.0.18, needed for PE-Core S & T models in OpenCLIP 3.0.0

July 21, 2025

  • ROPE support added to NaFlexViT. All models covered by the EVA base (eva.py) including EVA, EVA02, Meta PE ViT, timm SBB ViT w/ ROPE, and Naver ROPE-ViT can be now loaded in NaFlexViT when use_naflex=True passed at model creation time
  • More Meta PE ViT encoders added, including small/tiny variants, lang variants w/ tiling, and more spatial variants.
  • PatchDropout fixed with NaFlexViT and also w/ EVA models (regression after adding Naver ROPE-ViT)
  • Fix XY order with grid_indexing='xy', impacted non-square image use in 'xy' mode (only ROPE-ViT and PE impacted).

What's Changed

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.17...v1.0.18

Jul 10, 2025
Release v1.0.17

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
modelimg_sizetop1top5param_count
vit_large_patch16_rope_mixed_ape_224.naver_in1k22484.8497.122304.4
vit_large_patch16_rope_mixed_224.naver_in1k22484.82897.116304.2
vit_large_patch16_rope_ape_224.naver_in1k22484.6597.154304.37
vit_large_patch16_rope_224.naver_in1k22484.64897.122304.17
vit_base_patch16_rope_mixed_ape_224.naver_in1k22483.89496.75486.59
vit_base_patch16_rope_mixed_224.naver_in1k22483.80496.71286.44
vit_base_patch16_rope_ape_224.naver_in1k22483.78296.6186.59
vit_base_patch16_rope_224.naver_in1k22483.71896.67286.43
vit_small_patch16_rope_224.naver_in1k22481.2395.02221.98
vit_small_patch16_rope_mixed_224.naver_in1k22481.21695.02221.99
vit_small_patch16_rope_ape_224.naver_in1k22481.00495.01622.06
vit_small_patch16_rope_mixed_ape_224.naver_in1k22480.98694.97622.06
  • Some cleanup of ROPE modules, helpers, and FX tracing leaf registration
  • Preparing version 1.0.17 release

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.16...v1.0.17

Jun 26, 2025
Release v1.0.16

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.
ModelTop-1 AccTop-5 AccParams (M)Eval Seq Len
naflexvit_base_patch16_par_gap.e300_s576_in1k83.6796.4586.63576
naflexvit_base_patch16_parfac_gap.e300_s576_in1k83.6396.4186.46576
naflexvit_base_patch16_gap.e300_s576_in1k83.5096.4686.63576
  • 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

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.15...v1.0.16

Feb 23, 2025
Release v1.0.15

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

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.14...v1.0.15

Jan 19, 2025
Release v1.0.14

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

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.13...v1.0.14

Jan 9, 2025
Release v1.0.13

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

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.12...v1.0.13

Dec 3, 2024
Release v1.0.12

Nov 28, 2024

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.

What's Changed

New Contributors

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.11...v1.0.12

Oct 16, 2024
v1.0.11 Release

Quick turnaround from 1.0.10 to fix an error impacting 3rd party packages that still import through a deprecated path that isn't tested.

Oct 16, 2024

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.
modelimg_sizetop1top5param_count
mambaout_base_plus_rw.sw_e150_r384_in12k_ft_in1k38487.50698.428101.66
mambaout_base_plus_rw.sw_e150_in12k_ft_in1k28886.91298.236101.66
mambaout_base_plus_rw.sw_e150_in12k_ft_in1k22486.63298.156101.66
mambaout_base_tall_rw.sw_e500_in1k28884.97497.33286.48
mambaout_base_wide_rw.sw_e500_in1k28884.96297.20894.45
mambaout_base_short_rw.sw_e500_in1k28884.83297.2788.83
mambaout_base.in1k28884.7296.9384.81
mambaout_small_rw.sw_e450_in1k28884.59897.09848.5
mambaout_small.in1k28884.596.97448.49
mambaout_base_wide_rw.sw_e500_in1k22484.45496.86494.45
mambaout_base_tall_rw.sw_e500_in1k22484.43496.95886.48
mambaout_base_short_rw.sw_e500_in1k22484.36296.95288.83
mambaout_base.in1k22484.16896.6884.81
mambaout_small.in1k22484.08696.6348.49
mambaout_small_rw.sw_e450_in1k22484.02496.75248.5
mambaout_tiny.in1k28883.44896.53826.55
mambaout_tiny.in1k22482.73696.126.55
mambaout_kobe.in1k28881.05495.7189.14
mambaout_kobe.in1k22479.98694.9869.14
mambaout_femto.in1k28879.84895.147.3
mambaout_femto.in1k22478.8794.4087.3

Sept 2024

Oct 15, 2024
Release v1.0.10

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.
modelimg_sizetop1top5param_count
mambaout_base_plus_rw.sw_e150_r384_in12k_ft_in1k38487.50698.428101.66
mambaout_base_plus_rw.sw_e150_in12k_ft_in1k28886.91298.236101.66
mambaout_base_plus_rw.sw_e150_in12k_ft_in1k22486.63298.156101.66
mambaout_base_tall_rw.sw_e500_in1k28884.97497.33286.48
mambaout_base_wide_rw.sw_e500_in1k28884.96297.20894.45
mambaout_base_short_rw.sw_e500_in1k28884.83297.2788.83
mambaout_base.in1k28884.7296.9384.81
mambaout_small_rw.sw_e450_in1k28884.59897.09848.5
mambaout_small.in1k28884.596.97448.49
mambaout_base_wide_rw.sw_e500_in1k22484.45496.86494.45
mambaout_base_tall_rw.sw_e500_in1k22484.43496.95886.48
mambaout_base_short_rw.sw_e500_in1k22484.36296.95288.83
mambaout_base.in1k22484.16896.6884.81
mambaout_small.in1k22484.08696.6348.49
mambaout_small_rw.sw_e450_in1k22484.02496.75248.5
mambaout_tiny.in1k28883.44896.53826.55
mambaout_tiny.in1k22482.73696.126.55
mambaout_kobe.in1k28881.05495.7189.14
mambaout_kobe.in1k22479.98694.9869.14
mambaout_femto.in1k28879.84895.147.3
mambaout_femto.in1k22478.8794.4087.3

Sept 2024

Aug 23, 2024
Release v1.0.9

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
modeltop1top5param_countimg_size
vit_mediumd_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k87.43898.25664.11384
vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k86.60897.93464.11256
vit_betwixt_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k86.59498.0260.4384
vit_betwixt_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k85.73497.6160.4256
  • MobileNet-V1 1.25, EfficientNet-B1, & ResNet50-D weights w/ MNV4 baseline challenge recipe
modeltop1top5param_countimg_size
resnet50d.ra4_e3600_r224_in1k81.83895.92225.58288
efficientnet_b1.ra4_e3600_r240_in1k81.44095.7007.79288
resnet50d.ra4_e3600_r224_in1k80.95295.38425.58224
efficientnet_b1.ra4_e3600_r240_in1k80.40695.1527.79240
mobilenetv1_125.ra4_e3600_r224_in1k77.60093.8046.27256
mobilenetv1_125.ra4_e3600_r224_in1k76.92493.2346.27224

  • Add SAM2 (HieraDet) backbone arch & weight loading support

  • Add Hiera Small weights trained w/ abswin pos embed on in12k & fine-tuned on 1k

modeltop1top5param_count
hiera_small_abswin_256.sbb2_e200_in12k_ft_in1k84.91297.26035.01
hiera_small_abswin_256.sbb2_pd_e200_in12k_ft_in1k84.56097.10635.01

Aug 8, 2024

Jul 29, 2024
Release v1.0.8

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
modeltop1top1_errtop5top5_errparam_countimg_size
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k84.9915.0197.2942.70632.59544
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k84.77215.22897.3442.65632.59480
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k84.6415.3697.1142.88632.59448
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k84.31415.68697.1022.89832.59384
mobilenetv4_conv_aa_large.e600_r384_in1k83.82416.17696.7343.26632.59480
mobilenetv4_conv_aa_large.e600_r384_in1k83.24416.75696.3923.60832.59384
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k82.9917.0196.673.3311.07320
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k82.36417.63696.2563.74411.07256
modeltop1top1_errtop5top5_errparam_countimg_size
efficientnet_b0.ra4_e3600_r224_in1k79.36420.63694.7545.2465.29256
efficientnet_b0.ra4_e3600_r224_in1k78.58421.41694.3385.6625.29224
mobilenetv1_100h.ra4_e3600_r224_in1k76.59623.40493.2726.7285.28256
mobilenetv1_100.ra4_e3600_r224_in1k76.09423.90693.0046.9964.23256
mobilenetv1_100h.ra4_e3600_r224_in1k75.66224.33892.5047.4965.28224
mobilenetv1_100.ra4_e3600_r224_in1k75.38224.61892.3127.6884.23224
  • 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
modeltop1top1_errtop5top5_errparam_countimg_sizecrop_pct
test_efficientnet.r160_in1k47.15652.84471.72628.2740.361921.0
test_byobnet.r160_in1k46.69853.30271.67428.3260.461921.0
test_efficientnet.r160_in1k46.42653.57470.92829.0720.361600.875
test_byobnet.r160_in1k45.37854.62270.57229.4280.461600.875
test_vit.r160_in1k42.058.068.66431.3360.371921.0
test_vit.r160_in1k40.82259.17867.21232.7880.371600.875
  • Fix vit reg token init, thanks Promisery
  • Other misc fixes

June 24, 2024

  • 3 more MobileNetV4 hyrid weights with different MQA weight init scheme
modeltop1top1_errtop5top5_errparam_countimg_size
mobilenetv4_hybrid_large.ix_e600_r384_in1k84.35615.64496.8923.10837.76448
mobilenetv4_hybrid_large.ix_e600_r384_in1k83.99016.01096.7023.29837.76384
mobilenetv4_hybrid_medium.ix_e550_r384_in1k83.39416.60696.7603.24011.07448
mobilenetv4_hybrid_medium.ix_e550_r384_in1k82.96817.03296.4743.52611.07384
mobilenetv4_hybrid_medium.ix_e550_r256_in1k82.49217.50896.2783.72211.07320
mobilenetv4_hybrid_medium.ix_e550_r256_in1k81.44618.55495.7044.29611.07256
  • florence2 weight loading in DaViT model
Jun 19, 2024
Release v1.0.7

June 12, 2024

  • MobileNetV4 models and initial set of timm trained weights added:
modeltop1top1_errtop5top5_errparam_countimg_size
mobilenetv4_hybrid_large.e600_r384_in1k84.26615.73496.9363.06437.76448
mobilenetv4_hybrid_large.e600_r384_in1k83.80016.20096.7703.23037.76384
mobilenetv4_conv_large.e600_r384_in1k83.39216.60896.6223.37832.59448
mobilenetv4_conv_large.e600_r384_in1k82.95217.04896.2663.73432.59384
mobilenetv4_conv_large.e500_r256_in1k82.67417.32696.313.6932.59320
mobilenetv4_conv_large.e500_r256_in1k81.86218.13895.694.3132.59256
mobilenetv4_hybrid_medium.e500_r224_in1k81.27618.72495.7424.25811.07256
mobilenetv4_conv_medium.e500_r256_in1k80.85819.14295.7684.2329.72320
mobilenetv4_hybrid_medium.e500_r224_in1k80.44219.55895.384.6211.07224
mobilenetv4_conv_blur_medium.e500_r224_in1k80.14219.85895.2984.7029.72256
mobilenetv4_conv_medium.e500_r256_in1k79.92820.07295.1844.8169.72256
mobilenetv4_conv_medium.e500_r224_in1k79.80820.19295.1864.8149.72256
mobilenetv4_conv_blur_medium.e500_r224_in1k79.43820.56294.9325.0689.72224
mobilenetv4_conv_medium.e500_r224_in1k79.09420.90694.775.239.72224
mobilenetv4_conv_small.e2400_r224_in1k74.61625.38492.0727.9283.77256
mobilenetv4_conv_small.e1200_r224_in1k74.29225.70892.1167.8843.77256
mobilenetv4_conv_small.e2400_r224_in1k73.75626.24491.4228.5783.77224
mobilenetv4_conv_small.e1200_r224_in1k73.45426.54691.348.663.77224
  • 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.
  • Refactoring & improvements, especially related to classifier_reset and num_features vs head_hidden_size for forward_features() vs pre_logits
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Latest
v1.0.26
Source
@huggingface/pytorch-image-models
Tracking Since
May 31, 2019
Last fetched Apr 18, 2026
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