https://github.com/devsumanp/imageclassifier101
🪟 A beginner-friendly project for image classification using Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) with the CIFAR-10 dataset. Perfect for introducing neural networks and computer vision concepts.
https://github.com/devsumanp/imageclassifier101
cifar-10 cnn deep-learning image-classification machine-learning python tenserflow-image-classifier
Last synced: about 2 months ago
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🪟 A beginner-friendly project for image classification using Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) with the CIFAR-10 dataset. Perfect for introducing neural networks and computer vision concepts.
- Host: GitHub
- URL: https://github.com/devsumanp/imageclassifier101
- Owner: DevSumanP
- Created: 2024-11-19T07:49:56.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-11-19T08:06:58.000Z (6 months ago)
- Last Synced: 2024-11-19T09:19:11.945Z (6 months ago)
- Topics: cifar-10, cnn, deep-learning, image-classification, machine-learning, python, tenserflow-image-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 48.8 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **🪟 Image Classification Using Neural Networks**
This project demonstrates image classification using **Artificial Neural Networks (ANN)** and **Convolutional Neural Networks (CNN)** with the **CIFAR-10 dataset**. It includes dataset preprocessing, model building, training, and evaluation.
---
## **✨ Table of Contents**
1. [Introduction](#introduction)
2. [Dataset](#dataset)
3. [Technologies Used](#technologies-used)
4. [Project Structure](#project-structure)
5. [How to Run](#how-to-run)
6. [Code Explanation](#code-explanation)
7. [Results](#results)
8. [Conclusion](#conclusion)
---
## **📝 Introduction**
Image classification is the process of identifying and categorizing objects in an image. In this project, we use two types of neural networks:
- **ANN** (Artificial Neural Network): A basic model for image classification.
- **CNN** (Convolutional Neural Network): A more advanced model that captures image-specific features for better accuracy.
---
## **💾 Dataset**
We use the **CIFAR-10 dataset**, which contains:
- 60,000 images divided into 10 categories: *airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck*.
- Images are 32x32 pixels with 3 color channels (RGB).
---
## **💻 Technologies Used**
- **Python**: Programming language.
- **TensorFlow/Keras**: Framework for building and training neural networks.
- **Matplotlib**: Library for data visualization.
- **NumPy**: Library for numerical computations.
---
## **🗂️ Project Structure**
```
.
├── README.md # Documentation
└── Image_Classification_(Cifar_10).ipynb # Main script to execute the project
```
---
## **⚡ How to Run**
1. **Clone the repository**:
```bash
git clone https://github.com/DevSumanP/ImageClassifier101.git
cd ImageClassifier101
```
2. **Install dependencies**:
```bash
pip install tensorflow matplotlib numpy
```
3. **Run the script**:
```bash
python main.py
```
4. **Expected Output**:
- Model training logs.
- Accuracy metrics for both ANN and CNN.
- Visualizations of predictions.
---
## **⚡ Code Explanation**
1. **Data Preprocessing**:
- Normalize pixel values to range [0, 1] for faster and stable training.
- Reshape labels to make them compatible with the models.
2. **ANN Model**:
- A simple architecture with fully connected layers.
- Output layer uses a `softmax` activation function to predict probabilities for 10 classes.
3. **CNN Model**:
- Includes convolutional and pooling layers to extract features from images.
- Improves classification accuracy compared to ANN.
4. **Evaluation**:
- Evaluate models using test data and generate classification reports.
---
## **😃 Results**
| **Model** | **Accuracy** |
|--------------------|--------------|
| **ANN** | ~49% |
| **CNN** | ~70% |
---
## **Conclusion**
- **ANN** is suitable for simple datasets but lacks accuracy for images.
- **CNN** performs better due to its ability to extract spatial hierarchies (e.g., edges, textures).
- Image classification is an essential task in AI, with applications in self-driving cars, medical diagnostics, and more.
---
## **Future Work**
- Use data augmentation to improve accuracy.
- Experiment with deeper networks or transfer learning using pre-trained models.
- Apply this framework to a custom dataset for real-world applications.
---
## **🗒️ License**
This project is open-source under the [MIT License](LICENSE).