LongLLaVA

Efficiently extending multimodal large language models to 1,000 images.

CommonProductImageMultimodal LearningImage Processing

LongLLaVA is a multimodal large language model that extends efficiently to 1,000 images through a hybrid architecture, aimed at enhancing image processing and understanding capabilities. The model achieves effective learning and inference on large-scale image data through innovative architecture design, making it significant for fields like image recognition, classification, and analysis.

Supports efficient processing and analysis of large-scale image data.
Utilizes a hybrid architecture to optimize performance on image tasks.
Provides a flexible framework for model training and evaluation
supporting both single-image and multi-image tasks.
Achieves precise alignment between images and instructions
enhancing accuracy in image understanding.
Facilitates the construction of custom datasets and model training to meet specific needs.
Offers detailed documentation and scripts for users to quickly get started and utilize the model.

\The LongLLaVA model is designed for researchers and developers
particularly professionals focused on computer vision fields such as image recognition
classification
and analysis. It can assist them in enhancing model performance
optimizing image processing workflows

Used for image classification tasks to identify different categories of images.
Assists in medical image analysis for diagnostics and image annotation.
Used for image content review and filtering on social media platforms.

1. Visit the GitHub page to clone or download the LongLLaVA model.
2. Read the README documentation to understand the model's architecture and capabilities.
3. Follow the documentation to prepare a custom dataset or use a preset dataset.
4. Execute the pre-training script `bash Pretrain.sh` for initial model training.
5. Depending on your needs

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