DeepSeek-VL2: Mixture-of-Experts Vision-Language Models
What is the project about?
DeepSeek-VL2 is a series of advanced, large, Mixture-of-Experts (MoE) vision-language models. It's designed for multimodal understanding, meaning it can process and understand both images and text. It's an improvement over its predecessor, DeepSeek-VL.
What problem does it solve?
The project aims to provide powerful, open-source models that can perform complex tasks requiring understanding of both visual and textual information. It addresses the need for models that can perform well on tasks like:
- Visual Question Answering (VQA): Answering questions about images.
- Optical Character Recognition (OCR): Extracting text from images.
- Document/Table/Chart Understanding: Interpreting and extracting information from structured visual data.
- Visual Grounding: Locating objects within an image based on a textual description (and providing bounding box coordinates).
It solves these problems efficiently, achieving strong performance with a relatively small number of activated parameters, thanks to its MoE architecture.
What are the features of the project?
- Mixture-of-Experts (MoE) Architecture: This allows the model to be large overall but only activate a subset of its parameters for each input, improving efficiency.
- Three Model Variants:
DeepSeek-VL2-Tiny
,DeepSeek-VL2-Small
, andDeepSeek-VL2
, offering different sizes and performance levels (1.0B, 2.8B, and 4.5B activated parameters, respectively). - Multimodal Understanding: Processes both images and text.
- Visual Grounding Capability: Supports object localization with bounding box outputs using special tokens (
<|ref|>
,<|/ref|>
,<|det|>
,<|/det|>
). - Hugging Face Integration: Models are available on Hugging Face for easy download and use.
- Quick Start Examples: Provides code examples for simple inference with single and multiple images.
- Incremental Prefilling: Supports a memory-saving inference technique, crucial for running larger models on GPUs with limited memory.
- Gradio Demo: Includes a web demo for interactive use.
- VLMEvalKit Support: Compatible with a toolkit for evaluating vision-language models.
- Support for interleaved image-text input.
What are the technologies used in the project?
- Python: The primary programming language.
- PyTorch: The deep learning framework.
- Transformers (Hugging Face): Library for working with pre-trained models.
- Gradio: For creating the web demo.
- CUDA: For GPU acceleration.
What are the benefits of the project?
- State-of-the-Art Performance: Achieves competitive or state-of-the-art results compared to other open-source models.
- Efficiency: The MoE architecture allows for strong performance with fewer activated parameters, reducing computational cost.
- Open Source: The models and code are publicly available, promoting research and development.
- Commercial Use Supported: The license allows for commercial applications.
- Easy to Use: Integration with Hugging Face and provided examples make it relatively easy to get started.
- Flexibility: Different model sizes cater to various resource constraints.
What are the use cases of the project?
- Image Captioning: Generating descriptive text for images.
- Visual Question Answering Systems: Building interactive systems that answer questions about images.
- Document Processing Automation: Extracting information from scanned documents, forms, and charts.
- Robotics: Enabling robots to understand and interact with their visual environment.
- Accessibility Tools: Assisting visually impaired users by describing images and their content.
- Content Moderation: Identifying inappropriate or harmful content in images and videos.
- E-commerce: Improving product search and recommendation through visual understanding.
- Education: Creating interactive learning materials that combine visual and textual information.
- Research: A strong baseline for further research in vision-language modeling.
