SigLIP (shape optimized model) SigLIP model pre trained on WebLi at resolution 384x384. 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 class…
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