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Google's SigLIP is another alternative to openai's CLIP, and it  just got merged to 🤗transformers and it's super easy to use!
Google's SigLIP is another alternative to openai's CLIP, and it  just got merged to 🤗transformers and it's super easy to use!
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To celebrate this, I have created a repository including notebooks and bunch of Spaces on various SigLIP based projects 🥳  
To celebrate this, I have created a repository including notebooks and bunch of Spaces on various SigLIP based projects 🥳  
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Search for art 👉 
Search for art 👉 
resource
https://huggingface.co/spaces/merve/draw_to_search_art
space
merve/draw_to_search_art
https://huggingface.co/spaces/merve/draw_to_search_art
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Compare SigLIP with CLIP 👉 
Compare SigLIP with CLIP 👉 
resource
https://huggingface.co/spaces/merve/compare_clip_siglip
space
merve/compare_clip_siglip
https://huggingface.co/spaces/merve/compare_clip_siglip
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How does SigLIP work? 
How does SigLIP work? 
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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
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
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The image below is taken from CLIP, where this contrastive pre-training takes place with softmax, but SigLIP replaces softmax with sigmoid. 📎  
The image below is taken from CLIP, where this contrastive pre-training takes place with softmax, but SigLIP replaces softmax with sigmoid. 📎  
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Highlights from the paper on why you should use it ✨
Highlights from the paper on why you should use it ✨
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🖼️📝 Authors used medium sized B/16 ViT for image encoder and B-sized transformer for text encoder
🖼️📝 Authors used medium sized B/16 ViT for image encoder and B-sized transformer for text encoder
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😍 More performant than CLIP on zero-shot
😍 More performant than CLIP on zero-shot
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🗣️ Authors trained a multilingual model too!
🗣️ Authors trained a multilingual model too!
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⚡️ Super efficient, sigmoid is enabling up to 1M items per batch, but the authors chose 32k because the performance saturates after that
⚡️ Super efficient, sigmoid is enabling up to 1M items per batch, but the authors chose 32k because the performance saturates after that
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It's super easy to use thanks to transformers 👇 
It's super easy to use thanks to transformers 👇 
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```python
from transformers import pipeline
from PIL import Image
import requests

# load pipe
image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-base-patch16-256-i18n")

# load image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

# inference
outputs = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"])
outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs]
print(outputs)
```
from transformers import pipeline
from PIL import Image
import requests

# load pipe
image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-base-patch16-256-i18n")

# load image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

# inference
outputs = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"])
outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs]
print(outputs)
python
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For all the SigLIP notebooks on similarity search and indexing, you can check this [repository](
For all the SigLIP notebooks on similarity search and indexing, you can check this [repository](
link
https://github.com/merveenoyan/siglip
https://github.com/merveenoyan/siglip
text
) out. 🤗 
) out. 🤗 
Google's SigLIP is another alternative to openai's CLIP, and it  just got merged to 🤗transformers and it's super easy to use!
To celebrate this, I have created a repository including notebooks and bunch of Spaces on various SigLIP based projects 🥳  
Search for art 👉 https://huggingface.co/spaces/merve/draw_to_search_art
Compare SigLIP with CLIP 👉 https://huggingface.co/spaces/merve/compare_clip_siglip

How does SigLIP 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. 📎  

Highlights from the paper on why you should use it ✨
🖼️📝 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 because the performance saturates after that

It's super easy to use thanks to transformers 👇 
```python
from transformers import pipeline
from PIL import Image
import requests

# load pipe
image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-base-patch16-256-i18n")

# load image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

# inference
outputs = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"])
outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs]
print(outputs)
```
For all the SigLIP notebooks on similarity search and indexing, you can check this [repository](https://github.com/merveenoyan/siglip) out. 🤗 
👍
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❤️
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Julien Chaumond
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