siglip so400m patch14 224 EU AI Act Compliance Profile

SigLIP (shape-optimized model)

SigLIP model pre-trained on WebLi at resolution 224x224. It was introduced in the paper Sigmoid Loss for Language Image Pre-Training by Zhai et al. and first released in this repository.

This model has the SoViT-400m architecture, which is the shape-optimized version as presented in Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design by Alabdulmohsin et al.

Disclaimer: The team releasing SigLIP did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

SigLIP is CLIP, a multimodal model, with a better loss function. The sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. This allows further scaling up the batch size, while also performing better at smaller batch sizes.

A TLDR of SigLIP by one of the authors can be found here.

Intended uses & limitations

You can use the raw model for tasks like zero-shot image classification and image-text retrieval. See the model hub to look for other versions on a task that interests you.

How to use

Here is how to use this model to perform zero-shot image classification:

from PIL import Image
import requests
from transformers import AutoProcessor, AutoModel
import torch

model = AutoModel.from_pretrained("google/siglip-so400m-patch14-224")
processor = AutoProcessor.from_pretrained("google/siglip-so400m-patch14-224")

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

texts = ["a photo of 2 cats", "a photo of 2 dogs"]
inputs = processor(text=texts, images=ima

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Tags

Library