Technical solutions for efficient targeting using the VLM-R1
In the field of computer vision, accurately locating specific targets in complex images is a long-standing challenge. the VLM-R1 provides an innovative solution to this problem:
- Multimodal Fusion Architecture: The model can simultaneously parse image features and natural language descriptions through the visual-linguistic co-processing capability of Qwen2.5-VL.
- Enhanced Learning Optimization: The R1 method is used to train the model to understand spatial relationships in complex visual scenes more consistently.
- Specific steps::
- Prepare an image dataset containing the target object (COCO or custom dataset recommended)
- Define the task parameters using the rec.yaml configuration file provided with the project
- Setting the -num_generations parameter when running the grpo_rec.py training script controls the localization accuracy.
In practice, the batch size and gradient accumulation steps can be adjusted to balance the accuracy and memory usage, and it is recommended to increase the number of training rounds of num_train_epochs for particularly complex scenarios.
This answer comes from the articleVLM-R1: A Visual Language Model for Localizing Image Targets through Natural LanguageThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of accurately locating target objects in complex images?
