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Bump timm from 1.0.15 to 1.0.16 (#2390)
Bumps [timm](https://github.com/huggingface/pytorch-image-models) from
1.0.15 to 1.0.16.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/huggingface/pytorch-image-models/releases">timm's
releases</a>.</em></p>
<blockquote>
<h2>Release v1.0.16</h2>
<h2>June 26, 2025</h2>
<ul>
<li>MobileNetV5 backbone (w/ encoder only variant) for <a
href="https://ai.google.dev/gemma/docs/gemma-3n#parameters">Gemma 3n</a>
image encoder</li>
<li>Version 1.0.16 released</li>
</ul>
<h2>June 23, 2025</h2>
<ul>
<li>Add F.grid_sample based 2D and factorized pos embed resize to
NaFlexViT. Faster when lots of different sizes (based on example by <a
href="https://github.com/stas-sl">https://github.com/stas-sl</a>).</li>
<li>Further speed up patch embed resample by replacing vmap with matmul
(based on snippet by <a
href="https://github.com/stas-sl">https://github.com/stas-sl</a>).</li>
<li>Add 3 initial native aspect NaFlexViT checkpoints created while
testing, ImageNet-1k and 3 different pos embed configs w/ same
hparams.</li>
</ul>
<table>
<thead>
<tr>
<th align="left">Model</th>
<th align="center">Top-1 Acc</th>
<th align="center">Top-5 Acc</th>
<th align="center">Params (M)</th>
<th align="center">Eval Seq Len</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left"><a
href="https://hf.co/timm/naflexvit_base_patch16_par_gap.e300_s576_in1k">naflexvit_base_patch16_par_gap.e300_s576_in1k</a></td>
<td align="center">83.67</td>
<td align="center">96.45</td>
<td align="center">86.63</td>
<td align="center">576</td>
</tr>
<tr>
<td align="left"><a
href="https://hf.co/timm/naflexvit_base_patch16_parfac_gap.e300_s576_in1k">naflexvit_base_patch16_parfac_gap.e300_s576_in1k</a></td>
<td align="center">83.63</td>
<td align="center">96.41</td>
<td align="center">86.46</td>
<td align="center">576</td>
</tr>
<tr>
<td align="left"><a
href="https://hf.co/timm/naflexvit_base_patch16_gap.e300_s576_in1k">naflexvit_base_patch16_gap.e300_s576_in1k</a></td>
<td align="center">83.50</td>
<td align="center">96.46</td>
<td align="center">86.63</td>
<td align="center">576</td>
</tr>
</tbody>
</table>
<ul>
<li>Support gradient checkpointing for
<code>forward_intermediates</code> and fix some checkpointing bugs.
Thanks <a
href="https://github.com/brianhou0208">https://github.com/brianhou0208</a></li>
<li>Add 'corrected weight decay' (<a
href="https://arxiv.org/abs/2506.02285">https://arxiv.org/abs/2506.02285</a>)
as option to AdamW (legacy), Adopt, Kron, Adafactor (BV), Lamb, LaProp,
Lion, NadamW, RmsPropTF, SGDW optimizers</li>
<li>Switch PE (perception encoder) ViT models to use native timm weights
instead of remapping on the fly</li>
<li>Fix cuda stream bug in prefetch loader</li>
</ul>
<h2>June 5, 2025</h2>
<ul>
<li>Initial NaFlexVit model code. NaFlexVit is a Vision Transformer
with:
<ol>
<li>Encapsulated embedding and position encoding in a single module</li>
<li>Support for nn.Linear patch embedding on pre-patchified (dictionary)
inputs</li>
<li>Support for NaFlex variable aspect, variable resolution (SigLip-2:
<a
href="https://arxiv.org/abs/2502.14786">https://arxiv.org/abs/2502.14786</a>)</li>
<li>Support for FlexiViT variable patch size (<a
href="https://arxiv.org/abs/2212.08013">https://arxiv.org/abs/2212.08013</a>)</li>
<li>Support for NaViT fractional/factorized position embedding (<a
href="https://arxiv.org/abs/2307.06304">https://arxiv.org/abs/2307.06304</a>)</li>
</ol>
</li>
<li>Existing vit models in <code>vision_transformer.py</code> can be
loaded into the NaFlexVit model by adding the
<code>use_naflex=True</code> flag to <code>create_model</code>
<ul>
<li>Some native weights coming soon</li>
</ul>
</li>
<li>A full NaFlex data pipeline is available that allows training /
fine-tuning / evaluating with variable aspect / size images
<ul>
<li>To enable in <code>train.py</code> and <code>validate.py</code> add
the <code>--naflex-loader</code> arg, must be used with a NaFlexVit</li>
</ul>
</li>
<li>To evaluate an existing (classic) ViT loaded in NaFlexVit model w/
NaFlex data pipe:
<ul>
<li><code>python validate.py /imagenet --amp -j 8 --model
vit_base_patch16_224 --model-kwargs use_naflex=True --naflex-loader
--naflex-max-seq-len 256</code></li>
</ul>
</li>
<li>The training has some extra args features worth noting
<ul>
<li>The <code>--naflex-train-seq-lens'</code> argument specifies which
sequence lengths to randomly pick from per batch during training</li>
<li>The <code>--naflex-max-seq-len</code> argument sets the target
sequence length for validation</li>
<li>Adding <code>--model-kwargs enable_patch_interpolator=True
--naflex-patch-sizes 12 16 24</code> will enable random patch size
selection per-batch w/ interpolation</li>
<li>The <code>--naflex-loss-scale</code> arg changes loss scaling mode
per batch relative to the batch size, <code>timm</code> NaFlex loading
changes the batch size for each seq len</li>
</ul>
</li>
</ul>
<h2>May 28, 2025</h2>
<ul>
<li>Add a number of small/fast models thanks to <a
href="https://github.com/brianhou0208">https://github.com/brianhou0208</a>
<ul>
<li>SwiftFormer - <a
href="https://github.com/Amshaker/SwiftFormer">(ICCV2023) SwiftFormer:
Efficient Additive Attention for Transformer-based Real-time Mobile
Vision Applications</a></li>
<li>FasterNet - <a
href="https://github.com/JierunChen/FasterNet">(CVPR2023) Run, Don’t
Walk: Chasing Higher FLOPS for Faster Neural Networks</a></li>
<li>SHViT - <a href="https://github.com/ysj9909/SHViT">(CVPR2024) SHViT:
Single-Head Vision Transformer with Memory Efficient</a></li>
<li>StarNet - <a
href="https://github.com/ma-xu/Rewrite-the-Stars">(CVPR2024) Rewrite the
Stars</a></li>
<li>GhostNet-V3 <a
href="https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/ghostnetv3_pytorch">GhostNetV3:
Exploring the Training Strategies for Compact Models</a></li>
</ul>
</li>
<li>Update EVA ViT (closest match) to support Perception Encoder models
(<a
href="https://arxiv.org/abs/2504.13181">https://arxiv.org/abs/2504.13181</a>)
from Meta, loading Hub weights but I still need to push dedicated
<code>timm</code> weights
<ul>
<li>Add some flexibility to ROPE impl</li>
</ul>
</li>
<li>Big increase in number of models supporting
<code>forward_intermediates()</code> and some additional fixes thanks to
<a
href="https://github.com/brianhou0208">https://github.com/brianhou0208</a>
<ul>
<li>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</li>
</ul>
</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/huggingface/pytorch-image-models/commit/7101adb7ef562c9c2d63074a4e2098c15f5249e9"><code>7101adb</code></a>
Update README.md</li>
<li><a
href="https://github.com/huggingface/pytorch-image-models/commit/85b65f01299cb84561e8b7b038890186aa4556ce"><code>85b65f0</code></a>
Update version for 1.0.16 release</li>
<li><a
href="https://github.com/huggingface/pytorch-image-models/commit/1f69a52b7152f0585f8ea84f8b988ed35a682b74"><code>1f69a52</code></a>
Merge pull request <a
href="https://redirect.github.com/huggingface/pytorch-image-models/issues/2527">#2527</a>
from huggingface/mobilenetv5</li>
<li><a
href="https://github.com/huggingface/pytorch-image-models/commit/38286760112fa8ee6b6074c3c41e1e43ac033089"><code>3828676</code></a>
Make RmsNormAct sync with RmsNorm re default eps of 1e-6</li>
<li><a
href="https://github.com/huggingface/pytorch-image-models/commit/136440d9d410c1088978fb89b1fd04262f561980"><code>136440d</code></a>
Switch to 'same' padding emulation for the enc model as it should be
closer f...</li>
<li><a
href="https://github.com/huggingface/pytorch-image-models/commit/ddd3f99a7855f3c583d3a303c6c46c5a6d34f8b2"><code>ddd3f99</code></a>
Update test, encoder_only mode for backward test</li>
<li><a
href="https://github.com/huggingface/pytorch-image-models/commit/4cc7fdbd8843c7aac1b07f0b2ea264906ab8451d"><code>4cc7fdb</code></a>
Cleanup imports, mark MSFA as notrace</li>
<li><a
href="https://github.com/huggingface/pytorch-image-models/commit/857727ded8ff27d8b27738a4d56431229c979316"><code>857727d</code></a>
Simplify resolution check for improved script/trace compat</li>
<li><a
href="https://github.com/huggingface/pytorch-image-models/commit/e0cb66913666f6c8d06fa61748a169fbd0a7a6fd"><code>e0cb669</code></a>
Make features_only=True work with mnv5 & enc, uses
forward_intermediates()</li>
<li><a
href="https://github.com/huggingface/pytorch-image-models/commit/739b46cc65cd77568d78e19d836c577291f81e35"><code>739b46c</code></a>
Fixed pool size (16,16) because of of MSFA.</li>
<li>Additional commits viewable in <a
href="https://github.com/huggingface/pytorch-image-models/compare/v1.0.15...v1.0.16">compare
view</a></li>
</ul>
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