The following content is translated from the original text
Florence-2 is a lightweight visual language model open source by Microsoft under the MIT license. The model demonstrates powerful zero-sample and fine-tuning capabilities in tasks such as captioning, object detection, grounding, and segmentation.
Despite its small size, it achieves results comparable to models many times larger, such as Kosmos-2. The strength of this model lies not in its complex architecture, but in its large-scale FLD-5B dataset, which contains 126 million images and 5.4 billion comprehensive visual annotations.
You can try this model through HF Space or Google Colab.
unified representation
Visual tasks are diverse and vary in spatial hierarchy and semantic granularity. Instance segmentation provides detailed information about the location of objects within an image, but lacks semantic information. On the other hand, image captioning allows a deeper understanding of the relationships between objects without having to refer to their actual locations.
The authors of Florence-2 decided that instead of training a series of individual models capable of performing a single task, they would unify their representations and train a single model capable of performing more than 10 tasks. However, this requires a new data set.
Build a comprehensive data set
Unfortunately, there are currently no large unified data sets available. Existing large-scale datasets cover only limited tasks for a single image. SA-1B is a dataset used to train Segment Anything (SAM) and contains only masks. COCO, while supporting a wider range of tasks, is relatively small.
For more information, please see the original text, the original link, in the text description below this video
Thank you for watching this video. If you like it, please subscribe and like it. thank
Original text:https://blog.roboflow.com/florence-2/
Arxiv：https://arxiv.org/abs/2311.06242
HuggingFace：https://huggingface.co/microsoft/Florence-2-large
Oil tubing: