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The SigLIP model is a shape‑optimized model pre‑trained on the WebLI dataset, with a resolution of 384 × 384. It was introduced in the paper "Sigmoid Loss for Language‑Image Pre‑Training" by Zhai et al. and first released in Google Research's big_vision repository. SigLIP is a multimodal CLIP‑type model with an improved loss function that allows larger batch sizes without relying on global pairwise similarity normalization, and performs better with smaller batches. It is primarily used for zero‑shot image classification and image‑text retrieval. Training data include the WebLI dataset; images are resized to 384 × 384 and normalized, and text is tokenized and padded to 64 tokens. The model was trained for three days on 16 TPU‑v4 chips.
