PaliGemma: A versatile 3B VLM for transfer
Authors: name: Lucas Beyer, name: Andreas Steiner, name: André Susano Pinto, name: Alexander Kolesnikov, name: Xiao Wang, name: Daniel Salz, name: Maxim Neumann, name: Ibrahim Alabdulmohsin, name: Michael Tschannen, name: Emanuele Bugliarello, name: Thomas Unterthiner, name: Daniel Keysers, name: Skanda Koppula, name: Fangyu Liu, name: Adam Grycner, name: Alexey Gritsenko, name: Neil Houlsby, name: Manoj Kumar, name: Keran Rong, name: Julian Eisenschlos, name: Rishabh Kabra, name: Matthias Bauer, name: Matko Bošnjak, name: Xi Chen, name: Matthias Minderer, name: Paul Voigtlaender, name: Ioana Bica, name: Ivana Balazevic, name: Joan Puigcerver, name: Pinelopi Papalampidi, name: Olivier Henaff, name: Xi Xiong, name: Radu Soricut, name: Jeremiah Harmsen, name: Xiaohua Zhai

PaliGemma is an open Vision-Language Model (VLM) that is based on the
SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to
be a versatile and broadly knowledgeable base model that is effective to
transfer. It achieves strong performance on a wide variety of open-world tasks.
We evaluate PaliGemma on almost 40 diverse tasks including standard VLM
benchmarks, but also more specialized tasks such as remote-sensing and
segmentation.