mirrors/pytorch-image-models
1
0
Fork 0
mirror of https://github.com/huggingface/pytorch-image-models.git synced 2025-06-03 15:01:08 +08:00
Code Issues Projects Releases Wiki Activity

Add 384x384 mambaout_base_plus model weights

Browse Source
This commit is contained in:
Ross Wightman 2024-10-14 12:27:56 -07:00
parent 2703d155c8
commit c82ce86f8f
2 changed files with 8 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
README.md
View File

@ -17,6 +17,7 @@
|model |img_size|top1 |top5 |param_count|
|---------------------------------------------------------------------------------------------------------------------|--------|------|------|-----------|
|[mambaout_base_plus_rw.sw_e150_r384_in12k_ft_in1k](http://huggingface.co/timm/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](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 |
@ -41,8 +42,8 @@
* 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 (https://huggingface.co/timm/ViT-SO400M-16-SigLIP-i18n-256). OpenCLIP update pending.
* Add two ConNeXt 'Zepto' models & weights (one w/ overlapped stem and one w/ patch stem). Uses RMSNorm, smaller than previous 'Atto', 2.2M params
* 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](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
@ -54,6 +55,7 @@
* [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

4
timm/models/mambaout.py
View File

@ -500,6 +500,10 @@ default_cfgs = generate_default_cfgs({
'mambaout_base_plus_rw.sw_e150_in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
),
'mambaout_base_plus_rw.sw_e150_r384_in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), test_input_size=(3, 384, 384), crop_mode='squash', pool_size=(12, 12),
),
'mambaout_base_plus_rw.sw_e150_in12k': _cfg(
hf_hub_id='timm/',
num_classes=11821,