https://github.com/vivek-kumar-p/image-classification-model
Image-Classification-model is a Streamlit app that integrates MobileNetV2 and a CIFAR-10 model for image classification. Users can upload images and receive predictions with confidence scores from either model. It features a sleek navigation bar for easy switching and real-time results, ideal for both learning and practical use.
https://github.com/vivek-kumar-p/image-classification-model
cifar10 image-classification machine-learning python streamlit tensorflow
Last synced: about 2 months ago
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Image-Classification-model is a Streamlit app that integrates MobileNetV2 and a CIFAR-10 model for image classification. Users can upload images and receive predictions with confidence scores from either model. It features a sleek navigation bar for easy switching and real-time results, ideal for both learning and practical use.
- Host: GitHub
- URL: https://github.com/vivek-kumar-p/image-classification-model
- Owner: vivek-kumar-P
- Created: 2024-12-08T13:42:50.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-16T13:31:24.000Z (over 1 year ago)
- Last Synced: 2025-04-03T15:13:00.362Z (about 1 year ago)
- Topics: cifar10, image-classification, machine-learning, python, streamlit, tensorflow
- Language: Python
- Homepage:
- Size: 16.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🤖 Image Classification Model
This is a **Streamlit web app** that integrates **MobileNetV2** and a **CIFAR-10** model for image classification. Users can upload images and receive predictions with confidence scores from either model. It features a sleek navigation bar for easy switching and real-time results, which is ideal for learning and practical use.
**🌐 Live App:** [Streamlit Cloud Deployment](https://share.streamlit.io) (See deployment section)
## 🎯 Key Features
- **Dual Model Support**:
- **MobileNetV2 (ImageNet)**: Recognizes **1,000+ different classes** - animals, vehicles, objects, etc.
- **Custom CIFAR-10 Model**: Classifies into **10 specific categories** - airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck.
- **Intuitive Interface**:
- **Navigation Bar**: Seamlessly switch between models using sidebar menu
- **Real-Time Classification**: Upload images and get instant predictions with confidence scores
- **Beautiful UI**: Progress bars, formatted results, responsive design
- **Educational & Practical**:
- Learn about deep learning models and their performance
- Practical image classification for real-world applications
- Fully open-source and customizable
## 📋 Prerequisites
- **Python 3.10+** (Recommended: 3.11 for best compatibility)
- **Git** (for version control)
- **Web Browser** (Chrome, Firefox, Safari, Edge)
- **~2GB disk space** (for dependencies and models)
## 🚀 Installation & Setup
### Quick Start (Local Development)
1. **Clone the repository**:
```bash
git clone https://github.com/vivek-kumar-P/Image-Classification-model
cd Image-Classification-model
```
2. **Create virtual environment** (Python 3.11 recommended):
```bash
python -m venv venv
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate
```
3. **Install dependencies**:
```bash
pip install --upgrade pip
pip install -r requirements.txt
```
4. **Run the app**:
```bash
streamlit run app.py
```
5. **Access the app**:
Opens automatically at `http://localhost:8501`
### Windows Installation (Step-by-Step)
```powershell
# Navigate to project
cd "e:\code better\Image-Classification-model"
# Activate venv
.\venv\Scripts\Activate.ps1
# Install requirements
pip install -r requirements.txt
# Run app
streamlit run app.py
```
### Usage
1. **Select a Model**:
- Use the sidebar to choose between **MobileNetV2** or **CIFAR-10**
2. **Upload an Image**:
- Click "Choose an image..." and select JPG/PNG file
- Supported formats: JPEG, PNG, JPG
- Max file size: 200MB
3. **View Predictions**:
- Real-time classification results
- Top predictions with confidence scores
- Progress bars showing confidence levels
---
## 🐛 Known Issues & Solutions
### Issue 1: TensorFlow Installation Error
**Error:** `ERROR: Could not find a version that satisfies the requirement tensorflow`
**Cause:** TensorFlow 2.15+ requires Python 3.10-3.13. Python 3.14+ not yet supported.
**Solution:**
```bash
# Check Python version
python --version
# If Python 3.14+, use Python 3.11 instead
# Create new venv with Python 3.11
python3.11 -m venv venv
venv\Scripts\activate
pip install -r requirements.txt
```
### Issue 2: Missing Visual C++ Redistributable
**Error:** `OSError: [WinError 126] The specified module could not be found`
**Cause:** TensorFlow needs Microsoft Visual C++ runtime libraries.
**Solution:**
```
1. Download: https://aka.ms/vs/17/release/vc_redist.x64.exe
2. Run the installer (admin mode)
3. Restart your terminal/IDE
4. Reinstall TensorFlow: pip install tensorflow
```
### Issue 3: Port Already in Use
**Error:** `Port 8501 is already in use`
**Cause:** Another Streamlit app is running on the same port.
**Solution:**
```bash
# Use a different port
streamlit run app.py --server.port 8502
# Or kill existing process (Windows):
netstat -ano | findstr :8501
taskkill /PID /F
```
### Issue 4: Dependencies Conflict
**Error:** `error: subprocess-exited-with-error` with numpy version conflict
**Cause:** Incompatible version pinning in requirements.txt
**Solution:**
```bash
# Clear pip cache
pip cache purge
# Upgrade pip
pip install --upgrade pip
# Reinstall with fresh environment
pip install -r requirements.txt --no-cache-dir
```
### Issue 5: Streamlit Cloud Deployment Error
**Error:** TensorFlow installation fails on Streamlit Cloud
**Cause:** Streamlit Cloud trying to install with wrong Python version
**Solution:**
```
1. Delete the app from Streamlit Cloud
2. Redeploy and select Python 3.11 in Advanced Settings
3. Click Deploy
```
---
## 🌐 Streamlit Cloud Deployment
### Deploy Your App (Live on Web)
1. **Push code to GitHub**:
```bash
git add .
git commit -m "Your message"
git push origin main
```
2. **Go to Streamlit Cloud**:
- Visit [https://share.streamlit.io](https://share.streamlit.io)
- Sign in with GitHub
3. **Deploy New App**:
- Click "New app"
- Select repository: `vivek-kumar-P/Image-Classification-model`
- Branch: `main`
- Main file: `app.py`
4. **Configure Python Version** (IMPORTANT):
- Click "Advanced settings"
- Select **Python 3.11** from dropdown
- Click "Deploy"
5. **Wait for deployment** (2-3 minutes):
- App will be live at: `https://your-username-image-classification.streamlit.app`
### Troubleshooting Deployment
**Build fails with TensorFlow error?**
- Delete app → Redeploy with Python 3.11 selected
**Port/Process errors?**
- Streamlit Cloud handles this automatically, just redeploy
**Dependencies not installing?**
- Update requirements.txt with compatible versions
- Commit → Push → Redeploy
---
## 📊 System Architecture
### Local Development:
```
Your Computer
├─ Python 3.11
├─ Virtual Environment
├─ Dependencies (from requirements.txt)
├─ App code (app.py)
├─ Models (cifar10_model.h5)
└─ Runs on localhost:8501
```
### Streamlit Cloud:
```
Streamlit's Servers
├─ Python 3.11 (selected during deploy)
├─ Virtual Environment (auto-created)
├─ Dependencies (installed from requirements.txt)
├─ Your code (cloned from GitHub)
└─ Runs 24/7 at streamlit.app URL
```
**Key Point:** Cloud app is independent from local setup! Deleting local files won't affect the live app.
---
## 📦 Project Structure
```
Image-Classification-model/
├── app.py # Main Streamlit application
├── train.py # Model training script
├── cifar10_model.h5 # Pre-trained CIFAR-10 model
├── requirements.txt # Python dependencies
├── README.md # Documentation (this file)
├── .streamlit/
│ └── config.toml # Streamlit Cloud config
└── Testing Images/
├── CIFAR-10/ # Test images
└── MobileNetV2/ # Test images
```
---
## 📝 Recent Updates (January 28, 2026)
### Changes Made:
✅ Fixed dependency conflicts in `requirements.txt`
✅ Added flexible version constraints instead of strict pinning
✅ Added `.streamlit/config.toml` for Cloud deployment
✅ Updated app.py with better error handling
✅ Tested on Python 3.11 with TensorFlow 2.15.0
✅ Verified Streamlit Cloud deployment
### Problems Tackled:
1. **TensorFlow not available for Python 3.14** → Switched to Python 3.11
2. **Dependency version conflicts** → Updated requirements.txt with compatible versions
3. **Missing Visual C++ libraries** → Provided download and install instructions
4. **Port conflicts on local** → Added port configuration examples
5. **Streamlit Cloud deployment failures** → Added Python version selection guide
---
## 🔧 Troubleshooting Checklist
| Problem | Command | Status |
|---------|---------|--------|
| Wrong Python version | `python --version` | Check output |
| venv not activating | `venv\Scripts\activate.ps1` | PowerShell |
| Dependencies failing | `pip install --upgrade pip` | Then reinstall |
| App not starting | `streamlit run app.py --logger.level=error` | Debug mode |
| Port in use | `streamlit run app.py --server.port 8502` | Alt port |
| TensorFlow error | Use Python 3.11 | Fallback version |
---
## 📚 Model Information
### MobileNetV2 (ImageNet)
- **Classes:** 1,000+
- **Input:** 224×224 RGB images
- **Performance:** Fast, mobile-friendly
- **Use case:** General object detection
### CIFAR-10
- **Classes:** 10 (airplane, car, bird, cat, deer, dog, frog, horse, ship, truck)
- **Input:** 32×32 RGB images
- **Performance:** Specialized classification
- **Use case:** Specific object detection in 10 categories
---
## 🚀 Future Enhancements
- [ ] Add image download with predictions
- [ ] Add batch image processing
- [ ] Deploy custom model training interface
- [ ] Add prediction history
- [ ] Mobile app version
- [ ] Real-time webcam classification
---
## 📄 License
This project is open-source and available for educational and commercial use.
---
## 👥 Contributing
Feel free to:
- Fork the repository
- Create issues for bugs/features
- Submit pull requests
- Share feedback and improvements
---
## 🙏 Acknowledgments
- **Streamlit** - Web app framework
- **TensorFlow** - Deep learning library
- **Keras** - Neural network API
- **MobileNetV2** - Pre-trained model
- **CIFAR-10 Dataset** - Training data
---
## 📞 Support & Contact
For issues or questions:
1. Check the **Troubleshooting** section above
2. Review **Known Issues & Solutions**
3. Open an issue on GitHub
4. Contact the maintainer
---
**Last Updated:** January 28, 2026
**Status:** ✅ Production Ready for Streamlit Cloud