Update dependency timm to v1.0.24

[konflux-internal-p02]

This PR contains the following updates:

Package	Change	Age	Confidence
timm	==1.0.15 -> ==1.0.24
timm	==1.0.22 -> ==1.0.24

---

Release Notes

huggingface/pytorch-image-models (timm)

v1.0.24

Compare Source

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

• Add better NAdaMuon trained dpwee, dwee, dlittle (differential) ViTs with a small boost over previous runs
  • https://huggingface.co/timm/vit_dlittle_patch16_reg1_gap_256.sbb_nadamuon_in1k (83.24% top-1)
  • https://huggingface.co/timm/vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k (81.80% top-1)
  • https://huggingface.co/timm/vit_dpwee_patch16_reg1_gap_256.sbb_nadamuon_in1k (81.67% top-1)
• Add a ~21M param timm variant of the CSATv2 model at 512x512 & 640x640
  • https://huggingface.co/timm/csatv2_21m.sw_r640_in1k (83.13% top-1)
  • https://huggingface.co/timm/csatv2_21m.sw_r512_in1k (82.58% top-1)
• Factor non-persistent param init out of __init__ into a common method that can be externally called via init_non_persistent_buffers() after meta-device init.

Dec 12, 2025

• Add CSATV2 model (thanks https://github.com/gusdlf93) -- a lightweight but high res model with DCT stem & spatial attention. https://huggingface.co/Hyunil/CSATv2
• Add AdaMuon and NAdaMuon optimizer support to existing timm Muon impl. Appears more competitive vs AdamW with familiar hparams for image tasks.
• End of year PR cleanup, merge aspects of several long open PR
  • Merge differential attention (DiffAttention), add corresponding DiffParallelScalingBlock (for ViT), train some wee vits
    • https://huggingface.co/timm/vit_dwee_patch16_reg1_gap_256.sbb_in1k
    • https://huggingface.co/timm/vit_dpwee_patch16_reg1_gap_256.sbb_in1k
  • Add a few pooling modules, LsePlus and SimPool
  • Cleanup, optimize DropBlock2d (also add support to ByobNet based models)
• Bump unit tests to PyTorch 2.9.1 + Python 3.13 on upper end, lower still PyTorch 1.13 + Python 3.10

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

• Add val-interval argument by @​t0278611 in #​2606
• Add coord attn and some variants that I had lying around by @​rwightman in #​2617
• Distill fixups by @​rwightman in #​2598
• A simplification and some fixes for DropBlock2d. by @​rwightman in #​2620
• Other pooling... by @​rwightman in #​2621
• Experimenting with differential attention by @​rwightman in #​2314
• Differential + parallel attn by @​rwightman in #​2625
• AdaMuon impl w/ a few other ideas based on recent reading by @​rwightman in #​2626
• Csatv2 contribution by @​rwightman in #​2627
• Add HParams sections to hfdocs by @​rwightman in #​2630
• Upgrade GitHub Actions for Node 24 compatibility by @​salmanmkc in #​2633
• [BUG] Modify autocasting in fast normalization functions to handle optional weight params safely by @​tesfaldet in #​2631
• 'init_non_persistent_buffers' scheme by @​rwightman in #​2632
• Add docstrings to layer helper functions and modules by @​raimbekovm in #​2634
• refactor(scheduler): add type hints to CosineLRScheduler by @​haru-256 in #​2640
• A few misc weights to close out 2025 by @​rwightman in #​2639
• Update typing in other scheduler classes. Add unit tests. by @​rwightman in #​2641

New Contributors

• @​t0278611 made their first contribution in #​2606
• @​salmanmkc made their first contribution in #​2633
• @​tesfaldet made their first contribution in #​2631
• @​raimbekovm made their first contribution in #​2634
• @​haru-256 made their first contribution in #​2640

Full Changelog: huggingface/pytorch-image-models@v1.0.22...v1.0.24

v1.0.23

Compare Source

Dec 30, 2025

• Add better NAdaMuon trained dpwee, dwee, dlittle (differential) ViTs with a small boost over previous runs
  • https://huggingface.co/timm/vit_dlittle_patch16_reg1_gap_256.sbb_nadamuon_in1k (83.24% top-1)
  • https://huggingface.co/timm/vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k (81.80% top-1)
  • https://huggingface.co/timm/vit_dpwee_patch16_reg1_gap_256.sbb_nadamuon_in1k (81.67% top-1)
• Add a ~21M param timm variant of the CSATv2 model at 512x512 & 640x640
  • https://huggingface.co/timm/csatv2_21m.sw_r640_in1k (83.13% top-1)
  • https://huggingface.co/timm/csatv2_21m.sw_r512_in1k (82.58% top-1)
• Factor non-persistent param init out of __init__ into a common method that can be externally called via init_non_persistent_buffers() after meta-device init.

Dec 12, 2025

• Add CSATV2 model (thanks https://github.com/gusdlf93) -- a lightweight but high res model with DCT stem & spatial attention. https://huggingface.co/Hyunil/CSATv2
• Add AdaMuon and NAdaMuon optimizer support to existing timm Muon impl. Appears more competitive vs AdamW with familiar hparams for image tasks.
• End of year PR cleanup, merge aspects of several long open PR
  • Merge differential attention (DiffAttention), add corresponding DiffParallelScalingBlock (for ViT), train some wee vits
    • https://huggingface.co/timm/vit_dwee_patch16_reg1_gap_256.sbb_in1k
    • https://huggingface.co/timm/vit_dpwee_patch16_reg1_gap_256.sbb_in1k
  • Add a few pooling modules, LsePlus and SimPool
  • Cleanup, optimize DropBlock2d (also add support to ByobNet based models)
• Bump unit tests to PyTorch 2.9.1 + Python 3.13 on upper end, lower still PyTorch 1.13 + Python 3.10

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

• Add val-interval argument by @​t0278611 in #​2606
• Add coord attn and some variants that I had lying around by @​rwightman in #​2617
• Distill fixups by @​rwightman in #​2598
• A simplification and some fixes for DropBlock2d. by @​rwightman in #​2620
• Other pooling... by @​rwightman in #​2621
• Experimenting with differential attention by @​rwightman in #​2314
• Differential + parallel attn by @​rwightman in #​2625
• AdaMuon impl w/ a few other ideas based on recent reading by @​rwightman in #​2626
• Csatv2 contribution by @​rwightman in #​2627
• Add HParams sections to hfdocs by @​rwightman in #​2630
• Upgrade GitHub Actions for Node 24 compatibility by @​salmanmkc in #​2633
• [BUG] Modify autocasting in fast normalization functions to handle optional weight params safely by @​tesfaldet in #​2631
• 'init_non_persistent_buffers' scheme by @​rwightman in #​2632
• Add docstrings to layer helper functions and modules by @​raimbekovm in #​2634
• refactor(scheduler): add type hints to CosineLRScheduler by @​haru-256 in #​2640
• A few misc weights to close out 2025 by @​rwightman in #​2639
• Update typing in other scheduler classes. Add unit tests. by @​rwightman in #​2641

New Contributors

• @​t0278611 made their first contribution in #​2606
• @​salmanmkc made their first contribution in #​2633
• @​tesfaldet made their first contribution in #​2631
• @​raimbekovm made their first contribution in #​2634
• @​haru-256 made their first contribution in #​2640

Full Changelog: huggingface/pytorch-image-models@v1.0.22...v1.0.23

v1.0.22

Compare Source

Patch release for priority LayerScale initialization regression in 1.0.21

What's Changed

• Add some weights for efficientnet_x / efficientnet_h models by @​rwightman in #​2602
• Update result csvs by @​rwightman in #​2603
• Fix LayerScale ignoring init_values by @​Ilya-Fradlin in #​2605

New Contributors

• @​Ilya-Fradlin made their first contribution in #​2605

Full Changelog: huggingface/pytorch-image-models@v1.0.21...v1.0.22

v1.0.21

Compare Source

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

• Add calculate_drop_path_rates helper by @​rwightman in #​2589
• Review huggingface_hub integration by @​Wauplin in #​2592
• Adding device/dtype factory_kwargs to modules and models by @​rwightman in #​2591
• Consistent license handling throughout timm by @​alexanderdann in #​2585
• Add impl of Muon optimizer. Fix #​2580 by @​rwightman in #​2596
• Rename 'simple' flag for Muon to 'fallback' by @​rwightman in #​2599

New Contributors

• @​alexanderdann made their first contribution in #​2585

Full Changelog: huggingface/pytorch-image-models@v1.0.20...v1.0.21

v1.0.20

Compare Source

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

• DINOv3 (https://arxiv.org/abs/2508.10104) ConvNeXt and ViT models added. ConvNeXt models were mapped to existing timm model. ViT support done via the EVA base model w/ a new RotaryEmbeddingDinoV3 to match the DINOv3 specific RoPE impl
  • HuggingFace Hub: https://huggingface.co/collections/timm/timm-dinov3-68cb08bb0bee365973d52a4d
• MobileCLIP-2 (https://arxiv.org/abs/2508.20691) vision encoders. New MCI3/MCI4 FastViT variants added and weights mapped to existing FastViT and B, L/14 ViTs.
• MetaCLIP-2 Worldwide (https://arxiv.org/abs/2507.22062) ViT encoder weights added.
• SigLIP-2 (https://arxiv.org/abs/2502.14786) NaFlex ViT encoder weights added via timm NaFlexViT model.
• Misc fixes and contributions

What's Changed

• Pass init_values at hieradet_sam2 by @​hassonofer in #​2559
• Add mobileclip2 encoder weights by @​rwightman in #​2560
• Add support for Gemma 3n MobileNetV5 encoder weight loading by @​rwightman in #​2561
• Fix #​2562, add siglip2 naflex vit encoder weights by @​rwightman in #​2564
• fix: create results_dir if missing before saving results by @​zhima771 in #​2576
• feat(validate): add precision, recall, and F1 metrics by @​ha405 in #​2568
• Allow user to ask for features other than image and label in ImageDataset by @​grodino in #​2571
• Add MobileCLIP2 image encoders by @​rwightman in #​2578
• Add DINOv3 support by @​rwightman in #​2579

New Contributors

• @​hassonofer made their first contribution in #​2559
• @​zhima771 made their first contribution in #​2576
• @​ha405 made their first contribution in #​2568

Full Changelog: huggingface/pytorch-image-models@v1.0.19...v1.0.20

v1.0.19

Compare Source

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: huggingface/pytorch-image-models@v1.0.17...v1.0.18

v1.0.18

Compare Source

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: huggingface/pytorch-image-models@v1.0.17...v1.0.18

v1.0.17

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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: huggingface/pytorch-image-models@v1.0.16...v1.0.17

v1.0.16

Compare Source

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: huggingface/pytorch-image-models@v1.0.15...v1.0.16

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[openshift-ci]

[APPROVALNOTIFIER] This PR is NOT APPROVED

This pull-request has been approved by: konflux-internal-p02[bot]
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[openshift-ci]

Hi @konflux-internal-p02[bot]. Thanks for your PR.

I'm waiting for a red-hat-data-services member to verify that this patch is reasonable to test. If it is, they should reply with /ok-to-test on its own line. Until that is done, I will not automatically test new commits in this PR, but the usual testing commands by org members will still work. Regular contributors should join the org to skip this step.

Once the patch is verified, the new status will be reflected by the ok-to-test label.

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Instructions for interacting with me using PR comments are available here. If you have questions or suggestions related to my behavior, please file an issue against the kubernetes-sigs/prow repository.