File size: 2,473 Bytes
0583797 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
# TRIQA Hugging Face Demo - Quick Start Guide
## 🚀 Create Your Hugging Face Space
### Step 1: Create the Space
1. Go to https://huggingface.co/spaces
2. Click "Create new Space"
3. Fill in:
- **Space name**: `triqa-image-quality-assessment`
- **License**: MIT
- **SDK**: Gradio
- **Hardware**: CPU (or GPU if available)
- **Visibility**: Public
### Step 2: Upload Files
Upload these files from this folder to your Hugging Face Space:
#### Core Files (Upload these first):
- `app.py` - Main application
- `README.md` - Space description
- `requirements.txt` - Dependencies
- `package.json` - Node.js config
- `convnext_original.py` - Model architecture
- `convnext_finetune.py` - Model architecture
- `sample_image/` folder - Sample images
#### Model Files (Download from Box first):
1. Go to: https://utexas.box.com/s/8aw6axc2lofouja65uc726lca8b1cduf
2. Download the model files
3. Create these directories in your Space:
- `feature_models/`
- `Regression_Models/`
4. Upload the model files to their respective directories
### Step 3: Test the Demo
1. Wait for the build to complete (2-5 minutes)
2. Upload a sample image
3. Click "Assess Quality"
4. Verify you get a quality score (1-5 scale)
## 📁 File Structure in Your Space
```
your-space/
├── app.py
├── README.md
├── requirements.txt
├── package.json
├── convnext_original.py
├── convnext_finetune.py
├── sample_image/
│ ├── 233045618.jpg
│ ├── 25239707.jpg
│ ├── 44009500.jpg
│ ├── 5129172.jpg
│ └── 85119046.jpg
├── feature_models/
│ ├── convnext_tiny_22k_224.pth
│ └── triqa_quality_aware.pth
└── Regression_Models/
├── KonIQ_scaler.save
└── KonIQ_TRIQA.save
```
## 🎯 Expected Demo Features
- **Image Upload**: Drag & drop or click to upload
- **Quality Assessment**: Click button to get score
- **Sample Images**: Pre-loaded examples
- **Paper Links**: arXiv and IEEE links
- **Citation**: BibTeX citation
## 🔧 Troubleshooting
- **Build fails**: Check requirements.txt
- **Models not loading**: Verify model files are uploaded
- **File not found**: Check file paths and names
- **Memory issues**: Use CPU hardware
## 📞 Support
- GitHub: https://github.com/rajeshsureddi/triqa
- Paper: https://arxiv.org/pdf/2507.12687
Your demo will be live at: `https://huggingface.co/spaces/your-username/triqa-image-quality-assessment`
|