Technical features and advantages of the VLM-R1
Core technology features
- Based on the R1 training method: DeepSeek's R1 reinforcement learning approach is used to improve model stability
- Qwen2.5-VL base model: Uses the high-performance Qwen 2.5-VL-3B model as a base
- Supervised fine tuning (SFT): Fine tuning through specialized datasets
Performance Advantages
- Precise fingerprinting for understanding: Accurately understand and locate targets even in complex scenarios
- Better generalization capabilities: Ability to handle unseen referential expressions
- High training efficiency: Less time required for training than traditional visual language models
Practical Advantages
- Completely open source: Full training code and configuration available
- Easy to deploy: Support for multiple inference acceleration techniques
- Rich pre-training support: Built-in processing of mainstream visual linguistic datasets
Community Support
The project is actively maintained and the GitHub community is responsive, helping users to solve problems in real applications.
This answer comes from the articleVLM-R1: A Visual Language Model for Localizing Image Targets through Natural LanguageThe
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