Florence 2 VLM: Best Small Vision-Language Model?
In the video titled ‘Florence 2 – The Best Small VLM Out There?’ by Sam Witteveen, the discussion revolves around the newly released Florence 2 model by Microsoft, which was showcased at the CVPR conference. Florence 2 is a vision-language model (VLM) that boasts a dataset of 5.4 billion labels derived from 126 million images. Unlike previous models, Florence 2 is significantly smaller, with one version having around 200 million parameters and another around 700 million parameters, compared to the billions of parameters in other models.
The video highlights the comprehensive dataset created by Microsoft, which includes various types of labels such as bounding boxes, segmentation, and captioning. This extensive labeling allows Florence 2 to perform a wide range of tasks, including detailed image captioning, visual grounding, dense region captioning, and open vocabulary detection.
The architecture of Florence 2 follows a similar pattern to other VLMs, using an image encoder to generate representations that are processed through a transformer to produce text outputs. These outputs can include captions, bounding boxes, descriptions, and segmentation maps.
Sam demonstrates the capabilities of Florence 2 using the Hugging Face Spaces demo, showing how the model can generate simple and detailed captions, perform object detection, and segment images. He also tests the model with non-cherry-picked images to assess its performance in real-world scenarios.
The video also covers the practical applications of Florence 2, such as using it for OCR tasks, object detection with open vocabulary, and segmenting specific regions in images. Sam provides a Colab notebook for viewers to experiment with the model and fine-tune it for specific tasks.
Overall, Florence 2 is presented as a versatile and powerful VLM that excels in various vision tasks, making it a valuable tool for those working with image data. The model’s smaller size and extensive labeling make it efficient and effective for a wide range of applications.