The model shows outstanding advantages in multimodal understanding scenarios: in the field of e-commerce shopping guide, it can realize complex commands such as "find out the warranty information in the product detail page"; in automatic driving, it can accurately respond to spatial commands such as "navigate to the third parking space on the left". According to the technical white paper, in the real road scene test, the accuracy of the model's fingerprint recognition of vehicle targets reached 91.2%.
The project team provides a domain adaptation program, developers can modify the data_config/rec.yaml configuration file to access the custom data. Typical application cases include "turn off the lamp in the upper-right corner of the screen" in smart home, and "mark the scratched area on the surface of the steel plate" in industrial quality inspection, etc. The task completion rate of the model can be increased to more than 89% after fine-tuning the domain. The model has been fine-tuned by the domain to increase the task completion rate to more than 89%.
This answer comes from the articleVLM-R1: A Visual Language Model for Localizing Image Targets through Natural LanguageThe
