The project is designed to be a complete engineered solution for developer needs. The built-in automatic download script at the data level supports COCO2014 and RefCOCO datasets, and the supporting YAML configuration file standardizes the data pipeline. The training session provides multi-GPU distributed scripts to support Flash Attention acceleration and BF16 mixed precision, and the 8-card A100 server can complete the full-parameter fine-tuning of 3B parametric models.
In terms of deployment, the project integrates HuggingFace inference interface and Gradio demo system, users can directly call the trained model through the API. Tests show that the inference speed of a single RTX4090 graphics card reaches 23.5 FPS after Flash Attention is enabled, and the project also includes test scripts in the eval directory, which support automatic calculation of mAP, Recall@1 and other indicators to form a complete closed loop of model development.
This answer comes from the articleVLM-R1: A Visual Language Model for Localizing Image Targets through Natural LanguageThe
