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

• Add ROPE support to NaFlexVit (axial and mixed), and support most (all?) EVA based vit models & weights by @rwightman in #2552
• Support set_input_size() in EVA models by @rwightman in #2554

Full Changelog: v1.0.17...v1.0.18

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

• Add ROPE support to NaFlexVit (axial and mixed), and support most (all?) EVA based vit models & weights by @rwightman in #2552
• Support set_input_size() in EVA models by @rwightman in #2554

Full Changelog: v1.0.17...v1.0.18

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

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

What's Changed

• Adding Naver rope-vit compatibility to EVA ViT by @rwightman in #2529
• Update no_grad usage to inference_mode if possible by @GuillaumeErhard in #2534
• Add a min layer-decay scale clamp, and no optimization threshold to exclude groups from optimization by @rwightman in #2537
• Add stem_bias option to MNV5. Resolve the norm layer so can pass string. by @rwightman in #2538
• Add flag to enable float32 computation for normalization (norm + affine) by @rwightman in #2536
• fix: mnv5 conv_stem bias and GELU with approximate=tanh by @RyanMullins in #2533
• Fixup casting issues for weights/bias in fp32 norm layers by @rwightman in #2539
• Fix H, W ordering for xy indexing in ROPE by @rwightman in #2541
• Fix 3 typos in README.md by @robin-ede in #2544

New Contributors

• @GuillaumeErhard made their first contribution in #2534
• @RyanMullins made their first contribution in #2533
• @robin-ede made their first contribution in #2544

Full Changelog: v1.0.16...v1.0.17

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.

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

What's Changed

• Fix arg merging of sknet, old seresnet. Fix #2470 by @rwightman in #2471
• Fix onnx export by @rwightman in #2475
• Add local-dir: schema support for model loading (config + weights) from folder by @rwightman in #2476
• Fix: Allow img_size to be int or tuple in PatchEmbed by @sddongxh in #2477
• Add LightlyTrain Integration for Pretraining Support by @yutong-xiang-97 in #2474
• Check forward_intermediates features against forward_features output by @rwightman in #2483
• More models support forward_intermediates by @brianhou0208 in #2482
• Update README.md by @atharva-pathak in #2484
• remove download argument from torch_kwargs for torchvision ImageNet class by @ryan-caesar-ramos in #2486
• Update TNT-(S/B) model weights and add feature extraction support by @brianhou0208 in #2480
• Add EVA ViT based PE (Perceptual Encoder) impl by @rwightman in #2487
• Add SwiftFormer, SHViT, StarNet, FasterNet and GhostNetV3 by @brianhou0208 in #2499
• A cleaned up beit3 remap onto vision_transformer.py vit by @rwightman in #2503
• Initial NaFlex ViT model and training support by @rwightman in #2466
• Forgot to compact attention pool branches after verifying by @rwightman in #2507
• Throw exception on non-directory path for pretrained weights by @emmanuel-ferdman in #2510
• Add corrected_weight decay to several optimizers by @rwightman in #2511
• Doing some Claude enabled docstring, type annotation and other cleanup by @rwightman in #2504
• Fix #2513, be explicit about stream devices by @rwightman in #2515
• Update legacy AdamW impl so it has a multi-tensor impl like NAdamW (n… by @rwightman in #2517
• Fix head_dim reference in AttentionRope class of attention.py by @amorehead in #2519
• Refactor patch and pos embed resampling based on feedback from https://github.com/stas-sl by @rwightman in #2518
• Add initial weights for my first 3 naflexvit_base models by @rwightman in #2523
• Support gradient checkpointing in forward_intermediates() by @brianhou0208 in #2501
• Update README: add references for additional supported models by @brianhou0208 in #2526
• MobileNetV5 by @rwightman in #2527

New Contributors

• @sddongxh made their first contribution in #2477
• @yutong-xiang-97 made their first contribution in #2474
• @atharva-pathak made their first contribution in #2484
• @ryan-caesar-ramos made their first contribution in #2486
• @emmanuel-ferdman made their first contribution in #2510
• @amorehead made their first contribution in #2519

Full Changelog: v1.0.15...v1.0.16

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

• 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

What's Changed

• Fix metavar for --input-size by @JosuaRieder in #2417
• Add arguments to the respective argument groups by @JosuaRieder in #2416
• Add missing training flag to convert_sync_batchnorm by @collinmccarthy in #2423
• Fix num_classes update in reset_classifier and RDNet forward head call by @brianhou0208 in #2421
• timm: add all to init by @adamjstewart in #2399
• Fiddling with Kron (PSGD) optimizer by @rwightman in #2427
• Try to force numpy<2.0 for torch 1.13 tests, update newest tested torch to 2.5.1 by @rwightman in #2429
• Kron flatten improvements + stochastic weight decay by @rwightman in #2431
• PSGD: unify RNG by @ClashLuke in #2433
• Add vit so150m2 weights by @rwightman in #2439
• adapt_input_conv: add type hints by @adamjstewart in #2441
• SigLIP 2 by @rwightman in #2440
• timm.models: explicitly export attributes by @adamjstewart in #2442

New Contributors

• @collinmccarthy made their first contribution in #2423
• @ClashLuke made their first contribution in #2433

Full Changelog: v1.0.14...v1.0.15

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

• Fix nn.Module type hints by @adamjstewart in #2400
• Add missing paper title by @JosuaRieder in #2405
• fix 'timm recipe scripts' link by @JosuaRieder in #2404
• fix typo in EfficientNet docs by @JosuaRieder in #2403
• disable abbreviating csv inference output with ellipses by @JosuaRieder in #2402
• fix incorrect LaTeX formulas by @JosuaRieder in #2406
• VGG ConvMlp: fix layer defaults/types by @adamjstewart in #2409
• Implement --no-console-results in inference.py by @JosuaRieder in #2408
• LeViT safetensors load is broken by conversion code that wasn't deactivated by @rwightman in #2412
• A few more weights by @rwightman in #2413
• Fix typos by @JosuaRieder in #2415

New Contributors

• @adamjstewart made their first contribution in #2400

Full Changelog: v1.0.13...v1.0.14

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

• 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 weighgt 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

What's Changed

• Punch cache_dir through model factory / builder / pretrain helpers by @rwightman in #2356
• Yuweihao inception next atto merge by @rwightman in #2360
• Dataset trust remote tweaks by @rwightman in #2361
• Add --dataset-trust-remote-code to the train.py and validate.py scripts by @grodino in #2328
• Fix feature_info.reduction by @brianhou0208 in #2369
• Add caution to Adan. Add decouple decay option to LAMB. by @rwightman in #2357
• Switching to timm specific weight instances for open_clip image encoders by @rwightman in #2376
• Fix broken image link in Quickstart doc by @ariG23498 in #2381
• Supporting aimv2 encoders by @rwightman in #2379
• fix: minor typos in markdowns by @ruidazeng in #2382
• Add 384x384 in12k pretrain and finetune for convnext_nano by @rwightman in #2384
• Fixed unfused attn2d scale by @laclouis5 in #2387
• Fix MQA V2 by @laclouis5 in #2388
• Wrap torch checkpoint() fn to default use_reentrant flag to False and allow env var override by @rwightman in #2394
• Add half-precision (bfloat16, float16) support to train & validate scripts by @rwightman in #2397
• Merging wandb project name chages w/ addition by @rwightman in #2398

New Contributors

• @brianhou0208 made their first contribution in #2369
• @ariG23498 made their first contribution in #2381
• @ruidazeng made their first contribution in #2382
• @laclouis5 made their first contribution in #2387

Full Changelog: v1.0.12...v1.0.13

Release v1.0.12

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
  • https://huggingface.co/timm/mobilenetv4_conv_medium.e250_r384_in12k_ft_in1k
  • https://huggingface.co/timm/mobilenetv4_conv_medium.e250_r384_in12k
  • https://huggingface.co/timm/mobilenetv4_conv_medium.e180_ad_r384_in12k
  • https://huggingface.co/timm/mobilenetv4_conv_medium.e180_r384_in12k
• 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.

What's Changed

• mambaout.py: fixed bug by @NightMachinery in #2305
• Cleanup some amp related behaviour to better support different (non-cuda) devices by @rwightman in #2308
• Add NPU backend support for val and inference by @MengqingCao in #2109
• Update some clip pretrained weights to point to new hub locations by @rwightman in #2311
• ResNet vs MNV4 v1/v2 18 & 34 weights by @rwightman in #2316
• Replace deprecated positional argument with --data-dir by @JosuaRieder in #2322
• Fix typo in train.py: bathes > batches by @JosuaRieder in #2321
• Fix positional embedding resampling for non-square inputs in ViT by @wojtke in #2317
• Add trust_remote_code argument to ReaderHfds by @grodino in #2326
• Extend train epoch schedule by warmup_epochs if warmup_prefix enabled by @rwightman in #2325
• Extend existing unit tests using Cover-Agent by @mrT23 in #2331
• An impl of adafactor as per big vision (scaling vit) changes by @rwightman in #2320
• Add py.typed file as recommended by PEP 561 by @antoinebrl in #2252
• Add CODE_OF_CONDUCT.md and CITATION.cff files by @AlinaImtiaz018 in #2333
• Add some 384x384 small model weights by @rwightman in #2334
• In dist training, update loss running avg every step, sync on log by @rwightman in #2340
• Improve WandB logging by @sinahmr in #2341
• A few weights to merge Friday by @rwightman in #2343
• Update timm torchvision resnet weight urls to the updated urls in torchvision by @JohannesTheo in #2346
• More optimizer updates, add MARS, LaProp, add Adopt fix and more by @rwightman in #2347
• Cautious optimizer impl plus some typing cleanup. by @rwightman in #2349
• Add cautious mars, improve test reliability by skipping grad diff for… by @rwightman in #2351
• See if we can avoid some model / layer pickle issues with the aa attr in ConvNormAct by @rwightman in #2353

New Contributors

• @MengqingCao made their first contribution in #2109
• @JosuaRieder made their first contribution in #2322
• @wojtke made their first contribution in #2317
• @grodino made their first contribution in #2326
• @AlinaImtiaz018 made their first contribution in #2333
• @sinahmr made their first contribution in #2341
• @JohannesTheo made their first contribution in #2346

Full Changelog: v1.0.11...v1.0.12

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

• 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

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.

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