SigLIP 2 So400m SigLIP 2 extends the pretraining objective of SigLIP with prior, independently developed techniques into a unified recipe, for improved semantic understanding, localization, and dense features. Intended uses You can use the raw model for tasks like zero shot image classification and image text retrieval, or as a vision encoder for VLMs (and other vision tasks). Here is how to use this model to perform zero shot image classification: You can encode an image using the Vision Tower like so: For more code examples, we refer to the siglip2 documentation. Training procedure SigLIP 2 adds some clever training objectives on top of SigLIP: 1. Decoder loss 2. Global local and masked prediction loss 3. Aspect ratio and resolution adaptibility Training data SigLIP 2 is pre trained on the WebLI dataset (Chen et al., 2023). Compute The model was trained on up to 2048 TPU v5e chips. Evaluation results Evaluation of SigLIP 2 is shown below (taken from the paper). BibTeX entry and citation info
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