Object Recognition using Convolutional Neural Network (CNN)

Project Artefacts 

GitHub Repository - Deep Learning Projects - Capstone: CIFAR-10 Using CNN - This repository contains all project files, including the code, Jupyter Notebook, and project report. 

Jupyter Notebook: CIFAR-10 Using CNN Notebook - A detailed implementation notebook showcasing data preprocessing, CNN architecture design, model training, and evaluation. 

Project Report: CIFAR-10 Project Report - A comprehensive document detailing the project objectives, methodology, results, and conclusions. 

Highlights Technologies Used: Python, TensorFlow/Keras, Jupyter Notebook. 

Key Features

1. Dataset: The CIFAR-10 dataset contains 10 classes: airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks, providing a robust benchmark for image classification tasks.

2. Deep Learning Model: A Convolutional Neural Network (CNN) architecture is implemented for feature extraction and classification.

3. Model Optimization:

   • Techniques like data augmentation, dropout, and learning rate scheduling are applied to improve performance.

   • Multiple experiments with different architectures to find the most efficient and accurate model.

4. Evaluation Metrics: Performance is measured using metrics such as accuracy, precision, recall, and F1 score to ensure robust evaluation.

5. Visualization: Use of visual tools to demonstrate how the CNN interprets images and recognizes patterns for object classification.

Technical Highlights

1. Preprocessing:

   • Normalization of image data for faster convergence.

   • Data augmentation techniques such as rotation, flipping, and cropping to enhance model robustness.

2. Model Architecture:

   • Designed a CNN with multiple convolutional layers, pooling layers, and fully connected layers for classification.

   • Use of activation functions like ReLU for non-linearity and softmax for output probabilities.

3. Training and Validation:

   • Split the dataset into training and validation sets to prevent overfitting.

   • Monitored performance using training and validation accuracy/loss curves.

4. Tools and Frameworks:

   • TensorFlow/Keras: For building and training the CNN model.

   • Matplotlib and Seaborn: For visualizing performance metrics and results.

   • Google Colab and VSCode: For running experiments on GPUs.

Project Outcomes

• Achieved an accuracy of 70&-75% on the test set.

• Successfully demonstrated the application of CNNs in real-world object recognition tasks.

• Delivered a reusable and scalable model for similar image classification problems.

Conclusion

• This Capstone Project showcases the transformative capabilities of deep learning in object recognition, providing a solid foundation for future advancements in the field of computer vision. The use of the CIFAR-10 dataset demonstrates how even a relatively small dataset can yield powerful insights when combined with cutting-edge neural network architectures.

• Take a deep dive into the world of AI-driven vision systems with this exciting project!