https://github.com/shervinnd/cifar-10-image-classification-with-cnn
Description: Dive into this exciting project that builds a CNN with TensorFlow/Keras to classify CIFAR-10 images into 10 vibrant classes! 📸 It features data preprocessing, model training, stunning ROC curve visualizations 📊, sample predictions, and custom image classification
https://github.com/shervinnd/cifar-10-image-classification-with-cnn
auc-analysis batch-normalization cifar-10 cnn-model computer-vision convolutional-neural-networks custom-image-prediction deep-learning image-classification image-preprocessing keras machine-learning matplotlib-visualization numpy opencv python roc-curve scikit-learn softmax-classifier tensorflow
Last synced: about 1 month ago
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Description: Dive into this exciting project that builds a CNN with TensorFlow/Keras to classify CIFAR-10 images into 10 vibrant classes! 📸 It features data preprocessing, model training, stunning ROC curve visualizations 📊, sample predictions, and custom image classification
- Host: GitHub
- URL: https://github.com/shervinnd/cifar-10-image-classification-with-cnn
- Owner: shervinnd
- License: mit
- Created: 2025-08-23T07:11:50.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-08-23T07:25:47.000Z (about 1 month ago)
- Last Synced: 2025-08-24T10:09:38.638Z (about 1 month ago)
- Topics: auc-analysis, batch-normalization, cifar-10, cnn-model, computer-vision, convolutional-neural-networks, custom-image-prediction, deep-learning, image-classification, image-preprocessing, keras, machine-learning, matplotlib-visualization, numpy, opencv, python, roc-curve, scikit-learn, softmax-classifier, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 143 KB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CIFAR-10 Image Classification with CNN & ROC Analysis 🌟
Welcome to an exciting deep learning journey! 🚀 This project implements
a Convolutional Neural Network (CNN) using TensorFlow/Keras to classify
images from the CIFAR-10 dataset into 10 vibrant classes (e.g.,
airplane, dog, horse). It includes data preprocessing, model training,
ROC curve visualization, sample predictions, and custom image
classification. Let's dive in! 📸
## 📖 Project Overview
This repository contains a complete pipeline for image classification
using the CIFAR-10 dataset. The code is organized into four main
sections:
1. **CNN Model Training** 🧠: A CNN is built and trained to classify
CIFAR-10 images with high accuracy.
2. **ROC Curve Analysis** 📊: Evaluate model performance by plotting
ROC curves and calculating AUC for each class.
3. **Sample Visualization** 🖼️: Display one sample image per class with
true and predicted labels.
4. **Custom Image Prediction** 🐎: Classify a custom image (e.g.,
`horse.jpg`) using the trained model.
Powered by **Miracle ⚡**, this project is perfect for learning deep
learning concepts or building upon for advanced computer vision tasks!
😎
## 🎯 Features
- **Dataset**: CIFAR-10 with 50,000 training and 10,000 test images
across 10 classes.
- **Model**: A CNN with convolutional layers, batch normalization, max
pooling, and a softmax output layer.
- **Evaluation**: ROC curves with AUC scores for each class to assess
model performance.
- **Visualization**: A 2x5 grid of sample images with true and
predicted labels.
- **Custom Prediction**: Process and classify custom images (e.g.,
`horse.jpg`) with ease.
- **Tech Stack**: TensorFlow, Keras, NumPy, Matplotlib, Scikit-learn,
OpenCV.
## 🛠️ Installation
To run this project, ensure you have Python 3.x and the required
libraries installed. Follow these steps:
1. **Clone the Repository**:
``` bash
git clone https://github.com/shervinnd/cifar10-classification.git
cd cifar10-classification
```
2. **Install Dependencies**:
``` bash
pip install tensorflow numpy matplotlib scikit-learn opencv-python
```
3. **Prepare the Custom Image**:
- Place a custom image (e.g., `horse.jpg`) in the project
directory or update the `img_path` in the code.
4. **Run the Code**:
- Execute the `cifar10.py` script in a Jupyter Notebook or Python
environment:
``` bash
python cifar10.py
```
## 📂 File Structure
- `cifar10.py`: The main script containing the CNN model, ROC
analysis, sample visualization, and custom image prediction.
- `horse.jpg`: Example image for custom prediction (replace with your
own image).
## 🚀 Usage
1. **Training the Model**:
- The script loads and preprocesses the CIFAR-10 dataset, builds a
CNN, and trains it for 20 epochs.
- Check the model summary and training progress in the console.
2. **ROC Curve Analysis**:
- After training, the script generates ROC curves for each class,
showing AUC scores for performance evaluation.
3. **Sample Visualization**:
- A 2x5 grid displays one image per class with true and predicted
labels.
4. **Custom Image Prediction**:
- Replace `horse.jpg` with your image, ensure it's in the correct
path, and run the script to get the predicted class.
## 📊 Results
- **Model Performance**: The CNN achieves decent accuracy on CIFAR-10
after 20 epochs (tune hyperparameters for better results).
- **ROC Curves**: Visualize model performance with AUC scores per
class.
- **Sample Predictions**: See how the model predicts on test set
samples.
- **Custom Prediction**: Test the model on any 32x32 RGB image (e.g.,
`horse.jpg`).
## 💡 Improvements & Future Work
- Add **data augmentation** to improve model robustness.
- Implement **dropout** to prevent overfitting.
- Experiment with advanced architectures like **ResNet** or **VGG**.
- Enhance custom image preprocessing for better compatibility.
- Add **model evaluation** on the test set using `model.evaluate`
## 📚 References
- CIFAR-10 Dataset
- TensorFlow/Keras Documentation
- Scikit-learn ROC Metrics
## 🙌 Contributing
Contributions are welcome! 🎉 Feel free to open issues or submit pull
requests to improve the code, add features, or fix bugs. Let's make this
project even better together!
## 📧 Contact
Got questions or ideas? Reach out via GitHub issues 😊
**Powered by Miracle ⚡**