Florence-2-large
F
Florence 2 Large
Overview :
Florence-2-large, developed by Microsoft, is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of visual and visual-language tasks. The model can interpret simple text prompts to perform tasks such as image description, object detection, and segmentation. It is trained on the FLD-5B dataset, which contains 540 million images with 5.4 billion annotations, making it proficient in multi-task learning. Its sequence-to-sequence architecture enables it to perform well in both zero-shot and fine-tuning settings, proving to be a competitive vision foundation model.
Target Users :
The Florence-2-large model is suitable for developers and researchers who need to perform image analysis and understanding. Whether exploring the frontiers of visual recognition in academic research or implementing automatic image annotation and description in commercial applications, this model can provide powerful support.
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Use Cases
Automatically generate descriptive text for images on social media.
Provide object detection and classification services for product images on e-commerce websites.
In the field of autonomous driving, used for recognizing roads and traffic signs.
Features
Image Description: Generate descriptive text based on image content.
Object Detection: Identify objects in an image and annotate their locations.
Segmentation: Distinguish different regions in an image, such as objects and backgrounds.
Dense Region Description: Generate detailed descriptions for dense regions in an image.
Region Proposal: Propose regions in an image that may contain objects.
OCR: Recognize and extract text from an image.
OCR with Region: Perform text recognition combined with region information.
How to Use
Import necessary libraries, such as requests, PIL, Image, and transformers.
Load the Florence-2-large model from the pre-trained model using AutoModelForCausalLM and AutoProcessor.
Define the required task prompts, such as image description or object detection.
Load or obtain the image data to be processed.
Convert the text prompt and image data into a format acceptable to the model using the model and processor.
Call the model's generate method to generate results.
Use the processor's batch_decode method to convert the generated IDs into text.
Parse the generated text according to the task type using post-processing methods to obtain the final results.
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