Google’s latest innovation, a new family of vision language models called PaliGemma, is capable of producing text by receiving an image and a text input. Its architecture comprises the text decoder Gemma-2B and the image encoder SigLIP-So400m, which is also a model capable of understanding both text and visuals. On image-text data, the combined PaliGemma model can be pre-trained and easily refined, hence suitable for tasks like captioning or referencing segmentation.
The PaliGemma model was developed using the same Big_vision codebase used for models such as CapPa, SigLIP, LiT, BiT, and the original ViT. The PaliGemma release includes PT checkpoints, which are pre-trained and adaptable, Blend checkpoints, which are PT models adjusted for various tasks and FT checkpoints, which are a collection of refined models for distinct academic standards.
These models are available in three distinct precision levels (bfloat16, float16, and float32) and three different resolution levels (224×224, 448×448, and 896×896). Each repository contains checkpoints for a specific task and resolution with respective revisions for every precision. The models compatible with the original JAX implementation and hugging face transformers have their repositories.
The high-resolution models offer excellent quality but require more memory due to their longer input sequences. As such, the 224 versions are a suitable option for most users due to negligible quality differences.
PaliGemma is primarily a single-turn visual language model that performs best when tuned to a specific use case instead of being used for conversational purposes. Users can specify tasks for the model by using prefix qualifiers like ‘detect’ or ‘segment,’ allowing the models to fine-tune specific tasks.
PaliGemma can add captions to pictures, respond to questions about images, detect and segment entities within images, and reason and understand documents. However, its performance depends on the prompts used, as its pretrained models are designed to fine-tune specific tasks.
In conclusion, Google’s PaliGemma is a revolutionary model that brings the capacities of text and visuals into one platform, harnessing their combined strengths to perform a range of tasks. By understanding images and texts, this model seeks to redefine the boundaries of machine learning.
