How to Fine-tune PaliGemma 2

PaliGemma 2, an enhanced version of Google's PaliGemma vision-language model, integrates the SigLIP-So400m vision encoder with a Gemma 2 language model to process and generate text from images, supporting tasks like captioning and object detection. The tutorial outlines fine-tuning the model for JSON data extraction using a dataset of pallet manifests, prepared in JSONL format and annotated via Roboflow. It emphasizes choosing the right model checkpoint based on task complexity, data availability, and hardware capabilities. Memory optimization techniques such as LoRA and QLoRA are recommended for efficient fine-tuning, allowing the model to adapt to different tasks while managing computational demands. The tutorial also provides guidance on data preparation for tasks like object detection and instance segmentation, demonstrating how PaliGemma 2 can be adapted to a wide range of vision-language tasks through careful dataset preparation and model configuration.