VLM-R1
VLM-R1 is a stable and general reinforcement visual language model focused on visual understanding tasks.
Product Details
VLM-R1 is a visual language model based on reinforcement learning, focusing on visual understanding tasks such as Referring Expression Comprehension (REC). The model demonstrates excellent performance on both in-domain and out-of-domain data by combining R1 (Reinforcement Learning) and SFT (Supervised Fine-Tuning) methods. The main advantages of VLM-R1 include its stability and generalization capabilities, allowing it to perform well on a variety of visual language tasks. The model is built on Qwen2.5-VL and utilizes advanced deep learning technologies such as Flash Attention 2 to improve computing efficiency. VLM-R1 is designed to provide an efficient and reliable solution for visual language tasks, suitable for applications requiring precise visual understanding.
Main Features
How to Use
Target Users
This model is suitable for application scenarios that require efficient visual understanding, such as image annotation, intelligent customer service, autonomous driving and other fields. Its strong generalization ability and stability enable it to handle complex visual language tasks, providing developers with a reliable tool for building applications that require precise visual recognition.
Examples
In autonomous driving scenarios, VLM-R1 can be used to understand traffic signs and descriptions of road conditions.
In intelligent customer service, this model can parse users' descriptions of product pictures and provide accurate customer service support.
In the image annotation task, VLM-R1 can quickly locate target objects in images based on natural language descriptions.
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