CIFAR-10 CNN Image Classifier
A Convolutional Neural Network (CNN) built from scratch using TensorFlow/Keras to classify images from the CIFAR-10 dataset into 10 object categories.
This project focuses on understanding CNN design, training stability, regularization, and evaluation, without using pretrained models or transfer learning.
🚀 Project Overview
This project demonstrates:
- CNN architecture design from first principles
- Training and evaluation on the CIFAR-10 dataset
- Overfitting detection and mitigation
- Confusion matrix–based error analysis
- Clean, modular ML project structure
The goal is to gain hands-on understanding of deep learning fundamentals, rather than maximizing benchmark scores.
🧠 Dataset
CIFAR-10
- 60,000 color images (32×32)
- 10 classes:
- airplane, automobile, bird, cat, deer
- dog, frog, horse, ship, truck
- 50,000 training images
- 10,000 test images
🏗️ Model Architecture
- 3 Convolutional blocks
- Conv2D → Batch Normalization → ReLU → MaxPooling
- Classifier
- Dense(256) → Dropout(0.5)
- Dense(128) → Dropout(0.3)
- Dense(10 logits)
- Loss handled using
SparseCategoricalCrossentropy(from_logits=True)
Total parameters: ~1.3M
⚙️ Training Strategy
- Input normalization
- Data augmentation (horizontal flip, rotation, zoom)
- Batch Normalization for stable training
- Dropout for regularization
- Early stopping to prevent overfitting
Training was stopped automatically once validation performance stopped improving.
📊 Evaluation & Results
- Best validation accuracy: ~67%
- Small train–validation gap → good generalization
- Performance analyzed using a confusion matrix
Key observations:
- Strong performance on classes like automobile, frog, ship, and truck
- Expected confusion between visually similar classes (cat ↔ dog, deer ↔ horse)
- Confusion matrix used as a diagnostic tool rather than accuracy alone
🛠️ Installation
git clone https://github.com/revanthreddy0906/cifar10-cnn-image-classifier.git
cd cifar10-cnn-image-classifier
pip install -r requirements.txt
📌 Key Learnings
CNNs outperform dense networks for image data
Correct data pipelines are critical for stable training
Overfitting must be diagnosed using validation metrics
Confusion matrices provide deeper insight than accuracy alone
Regularization and early stopping are essential for generalization
📈 Future Improvements
Stronger data augmentation (MixUp / CutOut)
Learning rate scheduling
Residual connections (ResNet-style blocks)
Transfer learning with pretrained backbones
Per-class precision and recall analysis
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