This project demonstrates image classification on the CIFAR-10 dataset using a Convolutional Neural Network (CNN).
The CIFAR-10 dataset consists of 60,000 images across 10 distinct categories, such as airplanes, automobiles, birds, cats, and more.
- CIFAR-10 is a benchmark dataset for image classification tasks in machine learning and computer vision.
- It consists of 60,000 32x32 color images divided into 10 different classes.
- The dataset is split into 50,000 training images and 10,000 testing images.
- Common classes include airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.
- CIFAR-10 is frequently used for evaluating the performance of deep learning algorithms in image classification.
- CNNs are specifically designed for image-related tasks as they can capture spatial hierarchies in data.
- Convolution layers automatically extract features like edges, textures, and patterns from images.
- Pooling layers help in reducing the dimensionality and computation.
- CNNs are robust to translation and deformation of objects in images, making them ideal for datasets like CIFAR-10.
- Data Loading and Preprocessing
- Normalized pixel values to scale between 0 and 1.
- Converted class labels to one-hot encoding.
- Model Building
- Built a CNN with multiple Convolution, Pooling, and Dense layers.
- Used Dropout layers to prevent overfitting.
- Model Compilation
- Optimizer:
Adam - Loss Function:
Categorical Crossentropy - Metrics:
Accuracy
- Optimizer:
- Model Training
- Trained over multiple epochs on training data.
- Validated performance using validation data.
- Evaluation
- Plotted Accuracy and Loss graphs for both training and validation.
- Predicted new images and compared predictions with actual labels.
- Training and validation accuracy curves provide insights into how well the model generalizes.
- Loss curves help in diagnosing issues like overfitting or underfitting.
- Sample prediction visualizations help visually verify model performance on individual images.
- Image recognition systems in security cameras, robotics, and autonomous vehicles.
- Foundational learning for further exploration of Transfer Learning or more complex datasets.
- Academic research, tutorials, and educational projects on computer vision.
- Implement Transfer Learning using pre-trained models like ResNet, VGG, MobileNet for better accuracy.
- Experiment with Data Augmentation techniques to improve generalization.
- Build a web-based app using Flask or Streamlit to deploy the model.
- Try Ensemble Models or Attention Mechanisms for advanced performance.
- Dealing with small image sizes like 32x32 can make feature extraction harder.
- Class imbalance or noisy labels may impact performance.
- Computational resources can limit training deeper or more complex models.
- Provides hands-on experience with deep learning pipelines.
- Builds understanding of model evaluation techniques using metrics and visualizations.
- Teaches good practices like visualizing performance and verifying model predictions manually.
- Helps strengthen foundations for advancing towards more real-world computer vision problems.
plt.figure(figsize=(2,2))
plt.imshow(image)
plt.axis('off')
plt.title(f'Predicted: {class_names[predicted_class]} | Actual: {class_names[true_label]}')
plt.show()
📂 Project Structure
├── data/ # CIFAR-10 dataset (via keras.datasets)
├── model/ # CNN model and training scripts
├── results/ # Accuracy & Loss visualization plots
├── README.md # Project documentation
└── main.ipynb # Jupyter Notebook implementation
🎯 Results Summary
Achieved strong accuracy on both training and validation datasets.
Clear visualizations show model performance trends.
Individual sample predictions demonstrate model reliability.
👤 Author
Ashwin Kumar Data Analyst | AI Enthusiast | Deep Learning Explorer