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R1 Omni
Overview :
R1-Omni is an innovative multimodal emotion recognition model that enhances model reasoning and generalization capabilities through reinforcement learning. Developed based on HumanOmni-0.5B, it focuses on emotion recognition tasks and can perform emotion analysis using visual and audio modal information. Its main advantages include strong reasoning capabilities, significantly improved emotion recognition performance, and excellent performance on out-of-distribution data. This model is suitable for scenarios requiring multimodal understanding, such as sentiment analysis and intelligent customer service, and has significant research and application value.
Target Users :
This model is designed for researchers and developers, particularly those working on multimodal emotion recognition tasks. It helps them quickly build and optimize emotion recognition systems, improving model performance and interpretability. It can also be used in education to help students and researchers better understand the application of reinforcement learning in multimodal tasks.
Use Cases
In intelligent customer service systems, provide more accurate service by analyzing the emotions in customer voice and video.
In mental health applications, provide emotional guidance suggestions by analyzing users' emotional expressions.
In video content review, automatically detect negative emotions in videos to assist manual review.
Features
Enhances emotion recognition reasoning capabilities through reinforcement learning
Supports emotion analysis with full-modality input (video, audio)
Provides detailed reasoning processes, enhancing model interpretability
Exhibits excellent performance on out-of-distribution data, demonstrating strong generalization capabilities
Supports the integration of various pre-trained models, such as Whisper and Siglip
Provides detailed training and inference code for easy reproduction and extension by developers
Supports training and validation on various emotion datasets, such as DFEW and MAFW
Provides detailed performance metrics and visualization results for the model
How to Use
1. Download and install necessary dependencies, including PyTorch and multimodal models (such as Whisper, Siglip).
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2. Clone the R1-Omni code repository and set up the environment according to the README file.
3. Download pre-trained models (such as HumanOmni-0.5B, R1-Omni) and configure the paths.
4. Use the inference.py file for emotion inference with single video or multimodal input.
5. Adjust the model configuration file (config.json) as needed to adapt to different input data.
6. Use the training code (such as train.py) for model fine-tuning or custom training.
7. Use visualization tools (such as wandb) to view model training and inference results.
8. Integrate the model into specific application scenarios, such as intelligent customer service or video analysis systems, as needed.
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