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rwightman committed Oct 11, 2024
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## What's New

### Oct 11, 2024
* 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_in12k_ft_in1k](http://huggingface.co/timm/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](http://huggingface.co/timm/mambaout_base_plus_rw.sw_e150_in12k_ft_in1k)|224 |86.632|98.156|101.66 |
|[mambaout_base_tall_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_tall_rw.sw_e500_in1k) |288 |84.974|97.332|86.48 |
|[mambaout_base_wide_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_wide_rw.sw_e500_in1k) |288 |84.962|97.208|94.45 |
|[mambaout_base_short_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_short_rw.sw_e500_in1k) |288 |84.832|97.27 |88.83 |
|[mambaout_base.in1k](http://huggingface.co/timm/mambaout_base.in1k) |288 |84.72 |96.93 |84.81 |
|[mambaout_small_rw.sw_e450_in1k](http://huggingface.co/timm/mambaout_small_rw.sw_e450_in1k) |288 |84.598|97.098|48.5 |
|[mambaout_small.in1k](http://huggingface.co/timm/mambaout_small.in1k) |288 |84.5 |96.974|48.49 |
|[mambaout_base_wide_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_wide_rw.sw_e500_in1k) |224 |84.454|96.864|94.45 |
|[mambaout_base_tall_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_tall_rw.sw_e500_in1k) |224 |84.434|96.958|86.48 |
|[mambaout_base_short_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_short_rw.sw_e500_in1k) |224 |84.362|96.952|88.83 |
|[mambaout_base.in1k](http://huggingface.co/timm/mambaout_base.in1k) |224 |84.168|96.68 |84.81 |
|[mambaout_small.in1k](http://huggingface.co/timm/mambaout_small.in1k) |224 |84.086|96.63 |48.49 |
|[mambaout_small_rw.sw_e450_in1k](http://huggingface.co/timm/mambaout_small_rw.sw_e450_in1k) |224 |84.024|96.752|48.5 |
|[mambaout_tiny.in1k](http://huggingface.co/timm/mambaout_tiny.in1k) |288 |83.448|96.538|26.55 |
|[mambaout_tiny.in1k](http://huggingface.co/timm/mambaout_tiny.in1k) |224 |82.736|96.1 |26.55 |
|[mambaout_kobe.in1k](http://huggingface.co/timm/mambaout_kobe.in1k) |288 |81.054|95.718|9.14 |
|[mambaout_kobe.in1k](http://huggingface.co/timm/mambaout_kobe.in1k) |224 |79.986|94.986|9.14 |
|[mambaout_femto.in1k](http://huggingface.co/timm/mambaout_femto.in1k) |288 |79.848|95.14 |7.3 |
|[mambaout_femto.in1k](http://huggingface.co/timm/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](https://huggingface.co/timm/vit_so400m_patch14_siglip_378.webli_ft_in1k) - 89.42 top-1
* [vit_so400m_patch14_siglip_gap_378.webli_ft_in1k](https://huggingface.co/timm/vit_so400m_patch14_siglip_gap_378.webli_ft_in1k) - 89.03
* SigLIP SO400M ViT encoder from multi-lingual (i18n) patch16 @ 256x256 added
* Add two ConNeXt '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](https://huggingface.co/timm/convnext_zepto_rms_ols.ra4_e3600_r224_in1k) - 73.20 top-1 @ 224
* [convnext_zepto_rms.ra4_e3600_r224_in1k](https://huggingface.co/timm/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](http://hf.co/timm/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](http://hf.co/timm/mobilenetv3_large_150d.ra4_e3600_r256_in1k) - 81.81 @ 320, 80.94 @ 256
* [mobilenetv3_large_100.ra4_e3600_r224_in1k](http://hf.co/timm/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

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