Google Releases PaliGemma 2 Mix: An Open-Source Visual Language Model Supporting Multiple Tasks
Google has officially released PaliGemma 2 mix, a powerful multi-task visual language model. This model is the latest member of the Gemma series, capable of processing multiple visual-related tasks within a single model, including image description, optical character recognition (OCR), object detection, and image segmentation.
Key Features
Multi-Task Support
PaliGemma 2 mix supports various visual tasks:
- Image Description：Generates accurate and detailed image descriptions
- Optical Character Recognition (OCR)：Recognizes text content in images
- Object Detection：Detects and locates objects in images
- Image Segmentation：Performs precise semantic segmentation of images
- Document Understanding：Understands and analyzes document image content
- Open-Ended Visual Language Prompts：Supports flexible visual language interactions
Multiple Scale Options
To adapt to different application scenarios, the model offers three different scales:
- 3B parameter version：Suitable for resource-constrained scenarios
- 10B parameter version：Balances performance and resource consumption
- 28B parameter version：Provides the best performance
Flexible Resolution Support
The model supports two image input resolutions:
- 224px：Suitable for regular image processing tasks
- 448px：Suitable for scenarios requiring higher detail
Developer-Friendly Features
-
Framework Compatibility
- Supports Hugging Face Transformers
- Supports Keras
- Supports PyTorch
- Supports JAX
- Supports Gemma.cpp
-
Simple Task Switching
- Switches between different tasks through different prompts
- No additional model loading or switching required
Quick Start
Developers can start using PaliGemma 2 mix through the following ways:
-
Model Download
- Download the pre-trained model from Hugging Face or Kaggle
- View official documentation for detailed information
- Refer to example code repository for quick start
-
Development Framework Support
- Hugging Face Transformers - Using the most popular AI framework
- Keras - Officially recommended deep learning framework
- PyTorch - Flexible deep learning framework
- JAX - High-performance machine learning framework
- Gemma.cpp - C++ deployment scheme
-
Learning Resources
- Refer to inference tutorial for quick start
- Try custom dataset fine-tuning tutorial
- Experience model functionality through online demo
- Use Google Colab notebooks for experimentation
- Deploy through Vertex Model Garden
Future Outlook
Google indicates that the release of PaliGemma 2 mix is just the beginning. The team will continue to optimize model performance and improve user experience through community feedback. For users who need to fine-tune in specific domains, official documentation and example code are provided.