Upload README.md with huggingface_hub

README.md

@@ -1,14 +1,68 @@
 ---
-title: Triqa Iqa
-emoji: 📈
-colorFrom: red
-colorTo: green
+title: TRIQA Image Quality Assessment
+emoji: 🖼️
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 5.44.1
+sdk_version: 4.0.0
 app_file: app.py
 pinned: false
 license: mit
-short_description: 'TRIQA: Image Quality Assessment by Contrastive Pretraining o'
+short_description: Image Quality Assessment using ConvNeXt features
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# TRIQA: Image Quality Assessment
+
+TRIQA combines content-aware and quality-aware features from ConvNeXt models to predict image quality scores on a 1-5 scale.
+
+## Features
+
+- **Unified Framework**: Single interface combining content-aware and quality-aware feature extraction
+- **ConvNeXt Architecture**: Uses state-of-the-art ConvNeXt models for feature extraction
+- **Multi-scale Processing**: Processes images at two scales (original and half-size) for robust feature extraction
+- **Regression-based Prediction**: Uses trained regression models for quality score prediction
+- **Easy-to-use Interface**: Simple web interface for quality assessment
+
+## How It Works
+
+1. **Preprocessing**: Resize image to two scales (original + half-size)
+2. **Feature Extraction**: Extract content and quality features using ConvNeXt models
+3. **Prediction**: Combine features and predict quality score using regression model
+
+## Model Files
+
+Download the required model files from Box and place them in the appropriate directories:
+
+### Required Files:
+- `feature_models/convnext_tiny_22k_224.pth` - Content-aware model (170MB)
+- `feature_models/triqa_quality_aware.pth` - Quality-aware model (107MB)  
+- `Regression_Models/KonIQ_scaler.save` - Feature scaler
+- `Regression_Models/KonIQ_TRIQA.save` - Regression model (111MB)
+
+### Box Links:
+- [Download Model Files](https://utexas.box.com/s/8aw6axc2lofouja65uc726lca8b1cduf) - Place in `feature_models/` and `Regression_Models/` directories
+
+## Citation
+
+If you use this code in your research, please cite our paper:
+
+```bibtex
+@INPROCEEDINGS{11084443,
+  author={Sureddi, Rajesh and Zadtootaghaj, Saman and Barman, Nabajeet and Bovik, Alan C.},
+  booktitle={2025 IEEE International Conference on Image Processing (ICIP)}, 
+  title={Triqa: Image Quality Assessment by Contrastive Pretraining on Ordered Distortion Triplets}, 
+  year={2025},
+  volume={},
+  number={},
+  pages={1744-1749},
+  keywords={Image quality;Training;Deep learning;Contrastive learning;Predictive models;Feature extraction;Distortion;Data models;Synthetic data;Image Quality Assessment;Contrastive Learning},
+  doi={10.1109/ICIP55913.2025.11084443}}
+```
+
+### Paper Links:
+- **arXiv**: [https://arxiv.org/pdf/2507.12687](https://arxiv.org/pdf/2507.12687)
+- **IEEE Xplore**: [https://ieeexplore.ieee.org/abstract/document/11084443](https://ieeexplore.ieee.org/abstract/document/11084443)
+
+## License
+
+MIT License