Added Smooth-GmP - best Long-CLIP fine-tune yet! ✨

README.md

@@ -3,6 +3,16 @@ datasets:
 - SPRIGHT-T2I/spright_coco
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 ## A fine-tune of [BeichenZhang/LongCLIP-L](https://huggingface.co/BeichenZhang/LongCLIP-L) -- Long-CLIP ViT-L/14 expanded to 248 tokens.
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+## Update 12/AUG/2024:
+New *BEST* model, custom loss with label smoothing.
+Small gain for a diverse and large good quality dataset, but big relative gains for an overfit-prone fine-tune (small batch size, 1 GPU, narrow dataset of e.g. 'sneakers', etc.) are possible!
+Fine-tune your model with the provided code for GmP-Smooth: [https://github.com/zer0int/Long-CLIP](https://github.com/zer0int/Long-CLIP)
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6490359a877fc29cb1b09451/l3FYkaicihqXv5D9wLDAF.png)
+
+----
 
 The fine-tune has an improved ImageNet/ObjectNet accuracy of 0.89 (original Long-CLIP by the authors:~0.81)**.