lucataco / siglip

SigLIP proposes to replace the loss function used in CLIP by a simple pairwise sigmoid loss

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Input

Output

Run time and cost

This model runs on CPU hardware. Predictions typically complete within 38 seconds. The predict time for this model varies significantly based on the inputs.

Readme

SigLIP (base-sized 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.

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.

Training procedure

Training data SigLIP is pre-trained on the English image-text pairs of the WebLI dataset (Chen et al., 2023).

Preprocessing

Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).

Texts are tokenized and padded to the same length (64 tokens).

Compute

The model was trained on 16 TPU-v4 chips for three days.

Evaluation results

Evaluation of SigLIP compared to CLIP is shown below (taken from the paper).

@misc{zhai2023sigmoid,
      title={Sigmoid Loss for Language Image Pre-Training}, 
      author={Xiaohua Zhai and Basil Mustafa and Alexander Kolesnikov and Lucas Beyer},
      year={2023},
      eprint={2303.15343},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}