Florence-2-large

Model overview

The Florence-2 model is an advanced vision foundation model from Microsoft that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. It leverages the FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model.

The model comes in both base and large versions, with the large version having 0.77 billion parameters. There are also fine-tuned versions of both the base and large models available. The Florence-2-large-ft model in particular has been finetuned on a collection of downstream tasks.

Model inputs and outputs

Florence-2 can interpret simple text prompts to perform a variety of vision tasks, including captioning, object detection, and segmentation. The model takes in an image and a text prompt as input, and generates text or bounding boxes/segmentation maps as output, depending on the task.

Inputs

• Image: The model takes in an image as input.
• Text prompt: The model accepts a text prompt that describes the desired task, such as "Detect the objects in this image" or "Caption this image".

Outputs

• Text: For tasks like captioning, the model will generate text describing the image contents.
• Bounding boxes and labels: For object detection tasks, the model will output bounding boxes around detected objects along with class labels.
• Segmentation masks: The model can also output pixel-wise segmentation masks for semantic segmentation tasks.

Capabilities

Florence-2 is capable of performing a wide range of vision and vision-language tasks through its prompt-based approach. For example, the model can be used for image captioning, where it generates descriptive text about an image. It can also be used for object detection, where it identifies and localizes objects in an image. Additionally, the model can be used for semantic segmentation, where it assigns a class label to every pixel in the image.

One key capability of Florence-2 is its ability to adapt to different tasks through the use of prompts. By simply changing the text prompt, the model can be directed to perform different tasks, without requiring any additional fine-tuning.

What can I use it for?

The Florence-2 model can be useful in a variety of applications that involve vision and language understanding, such as:

• Content creation: The image captioning and object detection capabilities of Florence-2 can be used to automatically generate descriptions or annotations for images, which can be helpful for tasks like image search, visual storytelling, and content organization.

• Accessibility: The model's ability to generate captions and detect objects can be leveraged to improve accessibility for visually impaired users, by providing detailed descriptions of visual content.

• Robotics and autonomous systems: Florence-2's perception and language understanding capabilities can be integrated into robotic systems to enable them to better interact with and make sense of their visual environments.

• Education and research: Researchers and educators can use Florence-2 to explore the intersection of computer vision and natural language processing, and to develop new applications that leverage the model's unique capabilities.

Things to try

One interesting aspect of Florence-2 is its ability to handle a diverse range of vision tasks through the use of prompts. You can experiment with different prompts to see how the model's outputs change for various tasks. For example, you could try prompts like "<CAPTION>", "<OD>", or "<DENSE_REGION_CAPTION>" to see the model generate captions, object detection results, or dense region captions, respectively.

Another thing to try is fine-tuning the model on your own dataset. The Florence-2-large-ft model demonstrates the potential for further improving the model's performance on specific tasks through fine-tuning.