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Florence VL
Overview :
Florence-VL is a visual language model that enhances the processing capabilities of visual and language information by introducing generative visual encoders and deep breadth fusion technology. The significance of this technology lies in its ability to improve machines' understanding of images and text, achieving better performance in multimodal tasks. Florence-VL is developed based on the LLaVA project, providing code for pre-training and fine-tuning, model checkpoints, and demonstrations.
Target Users :
The target audience includes researchers and developers in the field of artificial intelligence, especially those focused on visual language models and multimodal learning. Florence-VL offers a robust model architecture and flexible configuration options, allowing researchers to train and optimize models according to their needs, while developers can utilize these models to quickly build and deploy multimodal applications.
Use Cases
Researchers use Florence-VL for joint representation learning of images and text to improve model performance in visual question answering tasks.
Developers leverage the pre-trained models provided by Florence-VL to quickly build an image annotation application.
In the education sector, Florence-VL is utilized to assist teaching by providing richer learning materials through the combination of images and text.
Features
Supports pre-training and fine-tuning to enhance the model's multimodal understanding capabilities.
Offers model checkpoints in two sizes, 3B and 8B, to accommodate different application needs.
Incorporates deep breadth fusion technology to improve the model's ability to handle complex visual language tasks.
Provides models hosted on the Hugging Face platform for easy user experience and application.
Includes detailed installation and usage documentation for developers to get started quickly.
Supports multimodal evaluation of the model using lmms-eval.
How to Use
1. Set up the environment: Create a Python virtual environment according to the instructions on the project page and install the dependencies.
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2. Download the dataset: Retrieve the pre-trained data and instruction data from the specified data sources.
3. Configure the training script: Set relevant variables in the training script based on personal data paths and hardware configurations.
4. Run the training: Execute the training script to initiate the pre-training and fine-tuning process of the model.
5. Evaluate the model: Use the lmms-eval tool to assess the performance of the trained model.
6. Apply the model: Deploy the trained model in practical applications such as image annotation and visual question answering.
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