Florence 2 Large Region To Category
Florence 2 Large: pinpoint objects in any image region
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Florence 2 Large Region To Category is a vision-language model that excels at recognizing objects within user-defined image regions. It takes an image and a region coordinate, then returns a category label for the object in that area. This makes it ideal for tasks like object detection, image annotation, and visual search.
Use this model when you need to identify what's in a specific part of an image, such as finding all objects in a photo or labeling components in a diagram. It works well with natural images, documents, and screenshots.
What sets Florence 2 Large apart is its ability to handle fine-grained categorization across diverse visual domains. It combines strong visual understanding with a large vocabulary of object categories, delivering accurate and descriptive labels for selected regions.
- Label objects in photos for inventory
- Identify components in technical diagrams
- Annotate regions in medical images
- Catalog items in e-commerce product shots
- Extract objects from screenshots
Consistently identifies objects in specified image areas.
Recognizes thousands of everyday and specialized objects.
Performs well on photos, documents, and graphics.
- Use tight bounding boxes around the object for best results.
- Avoid overlapping regions to prevent confusion.
- Try multiple region sizes if the object is not recognized.
- Combine with other models for full scene understanding.
- May struggle with very small or heavily occluded objects.
- Category labels are limited to its training vocabulary.
- Performance drops on abstract or artistic images.
List all objects in regions covering each item on this desk.
Identify the plant species in the central leaf region [200, 300, 400, 500].
Label all buttons in regions [10,10,100,50], [10,60,100,110], [10,120,100,170].
Sets a random seed for reproducible results across runs.
Limits category choices to a subset with high probability; useful for filtering unlikely labels.
Controls randomness in category selection; lower values make output more predictable.
Adjusts how strictly the model follows the region prompt; higher values enforce precision.
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What kind of objects can Florence 2 Large recognize?
It can recognize thousands of common objects, from everyday items like chairs and dogs to specialized ones like lab equipment.
How do I specify a region?
Provide coordinates as a bounding box in the format [x_min, y_min, x_max, y_max] relative to image dimensions.
Can it identify multiple objects in one region?
No, it returns a single category per region, so use separate regions for multiple objects.