pages/SigLIP/SigLIP.md

SigLIP just got merged to 🤗transformers and it's super easy to use! To celebrate this, I have created a repository on various SigLIP based projects!
But what is it and how does it work? SigLIP an vision-text pre-training technique based on contrastive learning. 
It jointly trains an image encoder and text encoder such that the dot product of embeddings are most similar for the appropriate text-image pairs.
The image below is taken from CLIP, where this contrastive pre-training takes place with softmax, but SigLIP replaces softmax with sigmoid. 📎

![image_1](image_1.jpg)

Highlights✨  
🖼️📝 Authors used medium sized B/16 ViT for image encoder and B-sized transformer for text encoder  
😍 More performant than CLIP on zero-shot  
🗣️ Authors trained a multilingual model too!  
⚡️ Super efficient, sigmoid is enabling up to 1M items per batch, but the authors chose 32k (see saturation on perf below)

![image_2](image_2.jpg)

Below you can find prior CLIP models and SigLIP across different image encoder sizes and their performance on different datasets 👇🏻 

![image_3](image_3.jpg)

With 🤗 Transformers integration there comes zero-shot-image-classification pipeline, makes SigLIP super easy to use! 

![image_4](image_4.jpg)

What to use SigLIP for? 🧐  
Honestly the possibilities are endless, but you can use it for image/text retrieval, zero-shot classification, training multimodal models!  
I have made a repository with notebooks and applications that are also hosted on [Spaces ](https://t.co/Ah1CrHVuPY).  
I have built ["Draw to Search Art"](https://t.co/DcmQWMc1qd) where you can input image (upload one or draw) and search among 10k images in wikiart! 
I've also built apps to [compare](https://t.co/m699TMvuW9)CLIP and SigLIP outputs.

![image_5](image_5.jpg)

> [!TIP]
Ressources:  
[Sigmoid Loss for Language Image Pre-Training](Sigmoid Loss for Language Image Pre-Training)  
by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer (2023)  
[GitHub](https://github.com/google-research/big_vision) 
[Hugging Face documentation](https://huggingface.co./docs/transformers/model_doc/siglip)   

> [!NOTE]
[Original tweet](https://twitter.com/mervenoyann/status/1745476609686089800) (January 11. 2024)