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) ineva.py, add RotaryEmbeddingMixed module for mixed mode, weights on HuggingFace Hub

• 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

Contributors

June 26, 2025

• MobileNetV5 backbone (w/ encoder only variant) forGemma 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 byhttps://github.com/stas-sl).
• Further speed up patch embed resample by replacing vmap with matmul (based on snippet byhttps://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.

• Support gradient checkpointing forforward_intermediates and fix some checkpointing bugs. Thankshttps://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 invision_transformer.py can be loaded into the NaFlexVit model by adding theuse_naflex=True flag tocreate_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 intrain.py andvalidate.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 tohttps://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-V3GhostNetV3: 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 dedicatedtimm weights
  • Add some flexibility to ROPE impl
• Big increase in number of models supportingforward_intermediates() and some additional fixes thanks tohttps://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 weightsforward_intermediates() thanks tohttps://github.com/brianhou0208
• Addlocal-dir: pretrained schema, can uselocal-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
• removedownload argument from torch_kwargs for torchvisionImageNet 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
• Fixhead_dim reference inAttentionRope class ofattention.py by@amorehead in#2519
• Refactor patch and pos embed resampling based on feedback fromhttps://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 inforward_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

Contributors

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 oftimm

Jan 27, 2025

• Add Kron Optimizer (PSGD w/ Kronecker-factored preconditioner)
  • Code fromhttps://github.com/evanatyourservice/kron_torch
  • See alsohttps://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: addall toinit 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

Contributors

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

Contributors

Jan 9, 2025

• Add support to train and validate in purebfloat16 orfloat16
• wandb project name arg added byhttps://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

• Addtorch.utils.checkpoint.checkpoint() wrapper intimm.models that defaultsuse_reentrant=False, unlessTIMM_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 fromhttps://github.com/apple/ml-aim, see on Hub:https://huggingface.co/models?search=timm%20aimv2
• Add PaliGemma2 encoders fromhttps://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 existingRmsNorm layer & fn to match standard formulation, use PT 2.5 impl when possible. Move old impl toSimpleNorm layer, it's LN w/o centering or bias. There were only twotimm models using it, and they have been updated.
• Allow override ofcache_dir arg for model creation
• Pass throughtrust_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 byhttps://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 withhf-hub: based loading, and thus will work with new TransformersTimmWrapperModel

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 inQuickstart 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

Contributors

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
  • Addlist_optimizers,get_optimizer_class,get_optimizer_info to reworkedcreate_optimizer_v2 fn to explore optimizers, get info or class
  • deprecateoptim.optim_factory, move fns tooptim/_optim_factory.py andoptim/_param_groups.py and encourage import viatimm.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, thankshttps://github.com/wojtke

Oct 31, 2024

Add a set of new very well trained ResNet & ResNet-V2 18/34 (basic block) weights. Seehttps://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 fromMengqingCao 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

Contributors

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 pathtimm.models.registry, increased priority of existing deprecation warnings to be visible
• Port weights of InternViT-300M (https://huggingface.co/OpenGVLab/InternViT-300M-448px) totimm asvit_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.

• 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

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.

• 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

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

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

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

Aug 8, 2024

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

July 28, 2024

• Addmobilenet_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

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

• Prototype ofset_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 toset_input_size,always_partition andstrict_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 severaltiny < .5M param models for testing that are actually trained on ImageNet-1k

• Fix vit reg token init, thanksPromisery
• Other misc fixes

June 24, 2024

• 3 more MobileNetV4 hyrid weights with different MQA weight init scheme

• florence2 weight loading in DaViT model