How to Fine-tune Florence-2 for Object Detection Tasks

Florence-2, an open-source vision-language model by Microsoft, showcases robust zero-shot and fine-tuning capabilities for tasks such as captioning, object detection, grounding, and segmentation. Although it may lack domain-specific knowledge, particularly for medical or satellite imagery, fine-tuning with custom datasets can enhance its performance for specific use cases. This tutorial outlines the process of fine-tuning Florence-2 on object detection datasets, leveraging techniques like LoRA to optimize training efficiency by reducing trainable parameters. The tutorial involves configuring the environment, setting up necessary tokens, and utilizing tools like Google Colab with GPU support. The model's adaptability allows it to maintain detection capabilities for base classes like those in the COCO dataset, even after fine-tuning. While Florence-2 may exhibit lower mean Average Precision (mAP) compared to specialized models like YOLO, its versatility in handling multiple tasks and ability to detect multiple object classes simultaneously offers significant advantages for diverse applications. The tutorial concludes by guiding users on deploying the fine-tuned model using the Roboflow platform and highlights the benefits of Florence-2's multi-tasking capabilities, including object character recognition (OCR).