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README.md

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   This is the weights repository for CVPR 2025 JarvisIR paper.
   Contains all pretrained model weights used in the paper.
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+---
+
+# JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration
+
+## Model Description
+
+JarvisIR is a novel vision-language model (VLM) based intelligent image restoration system designed for autonomous driving perception under adverse weather conditions. The system uses a VLM as a central controller to dynamically coordinate multiple expert restoration models for handling complex weather degradations including rain, fog, night scenes, and snow.
+
+## Key Features
+
+- **VLM-based Controller**: First framework to use vision-language models for controlling image restoration workflows
+- **Multi-Expert Coordination**: Dynamic scheduling of specialized restoration models (denoising, super-resolution, deraining, etc.)
+- **Weather-Adaptive**: Handles multiple weather degradations: night/low-light, rain, fog, snow scenarios
+- **Two-Stage Training**: Supervised Fine-Tuning (SFT) + Mixed-Rank Reward-based Human Feedback (MRRHF) alignment
+
+## Model Architecture
+
+The system consists of:
+1. **VLM Controller**: Based on LLaVA-v1.5-7B for task planning and model selection
+2. **Expert Models**: Specialized restoration networks for different degradation types
+3. **Reward Models**: Multiple IQA models for quality assessment and alignment
+
+## Training Data
+
+- **CleanBench-Synthetic**: 150K synthetic degraded images with annotations
+- **CleanBench-Real**: 80K real-world adverse weather images for alignment training
+- **Coverage**: Four main weather scenarios (night, rain, fog, snow) with multiple degradation combinations
+
+## Performance
+
+- **50% average improvement** in perception metrics on CleanBench-Real compared to existing all-in-one methods
+- Superior performance across all weather conditions tested
+- Enhanced robustness and generalization to real-world scenarios
+
+## Intended Use
+
+**Primary Applications:**
+- Autonomous driving perception systems
+- Multi-weather image restoration pipelines
+- Research in vision-language model applications
+
+
+## Model Checkpoints
+
+This repository contains weights for:
+- `jarvisir`: Model after supervised fine-tuning and MRRHF alignment stage
+- `expert-tools/`: Individual specialist restoration model weights
+
+
+## Citation
+
+```bibtex
+@inproceedings{jarvisir2025,
+  title={JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration},
+  author={Lin, Yunlong and Lin, Zixu and Chen, Haoyu and Pan, Panwang and Li, Chenxin and Chen, Sixiang and Kairun, Wen and Jin, Yeying and Li, Wenbo and Ding, Xinghao},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2025}
+}
+```
+
+## Related Resources
+
+- **Project Page**: https://cvpr2025-jarvisir.github.io/
+- **Code Repository**: https://github.com/LYL1015/JarvisIR
+- **Paper**: https://arxiv.org/pdf/2504.04158
+
+## Acknowledgments
+
+This work advances the field of intelligent image restoration by combining vision-language models with expert system coordination, specifically targeting autonomous driving applications under challenging weather conditions.
+