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VLM R1
VLM-R1
V
VLM R1
AI ModelImage Editing#Visual-Language Model#Reinforcement Learning#Image Understanding#Deep Learning#Natural Language ProcessingStandard PicksOpen Source
Overview :
VLM-R1 is a reinforcement learning-based visual-language model focused on visual understanding tasks, such as Referring Expression Comprehension (REC). By combining Reinforcement Learning (R1) and Supervised Fine-Tuning (SFT) methods, this model demonstrates excellent performance on both in-domain and out-of-domain data. The main advantages of VLM-R1 include its stability and generalization ability, enabling it to excel in various visual-language tasks. Built upon Qwen2.5-VL, the model leverages advanced deep learning techniques like Flash Attention 2 to enhance computational efficiency. VLM-R1 aims to provide an efficient and reliable solution for visual-language tasks, suitable for applications requiring precise visual understanding.
Target Users :
This model is suitable for applications requiring efficient visual understanding, such as image annotation, intelligent customer service, and autonomous driving. Its strong generalization and stability enable it to handle complex visual-language tasks, providing developers with a reliable tool for building applications that require precise visual recognition.
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Use Cases
In autonomous driving scenarios, VLM-R1 can be used to understand descriptions of traffic signs and road conditions.
In intelligent customer service, the model can parse user descriptions of product images to provide accurate support.
In image annotation tasks, VLM-R1 can quickly locate target objects in images based on natural language descriptions.
Features
Supports referring expression comprehension tasks, enabling accurate identification of specific objects in images.
Provides a GRPO (Guided Reinforcement Policy Optimization) training method to enhance the model's generalization ability.
Compatible with various data formats, supporting customized data loading and processing.
Offers detailed training and evaluation scripts for easy onboarding and extension.
Supports various hardware acceleration options, such as BF16 and Flash Attention 2, to optimize training efficiency.
How to Use
1. Clone the VLM-R1 repository and install dependencies: `git clone https://github.com/om-ai-lab/VLM-R1.git` and run `bash setup.sh`.
2. Prepare the dataset by downloading COCO images and annotation files for the referring expression comprehension task.
3. Configure data paths and model parameters by editing the `rec.yaml` file to specify the dataset path.
4. Train the model using the GRPO method: run `bash src/open-r1-multimodal/run_grpo_rec.sh`.
5. Evaluate model performance: run `python test_rec_r1.py` for model evaluation.
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