https://github.com/sisolieri/cnn-cifar10-classification
Classification of CIFAR-10 images using a CNN model as part of my Master's in Data Science. 12 experiments were conducted to improve accuracy, achieving 90.7% with data augmentation. Future work focuses on refining transfer learning techniques.
https://github.com/sisolieri/cnn-cifar10-classification
cifar10 cnn-classification convolutional-neural-networks data-augmentation deep-learning image-classification keras machine-learning python tensorflow transfer-learning
Last synced: 4 months ago
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Classification of CIFAR-10 images using a CNN model as part of my Master's in Data Science. 12 experiments were conducted to improve accuracy, achieving 90.7% with data augmentation. Future work focuses on refining transfer learning techniques.
- Host: GitHub
- URL: https://github.com/sisolieri/cnn-cifar10-classification
- Owner: sisolieri
- License: mit
- Created: 2024-10-14T20:58:52.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-10-14T22:21:17.000Z (9 months ago)
- Last Synced: 2025-03-30T23:43:56.414Z (4 months ago)
- Topics: cifar10, cnn-classification, convolutional-neural-networks, data-augmentation, deep-learning, image-classification, keras, machine-learning, python, tensorflow, transfer-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 7.05 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CNN CIFAR-10 Image Classification
This project was developed as part of my Master's program in Data Science and Artificial Intelligence. It focuses on classifying images from the CIFAR-10 dataset using Convolutional Neural Networks (CNNs). The primary goal was to achieve at least 80% accuracy on the test set through architectural modifications alone. I conducted a total of 12 experiments, exceeding the minimum requirement of 10 different experiments.
## Project Overview
The CIFAR-10 dataset consists of 60,000 32x32 color images across 10 classes. The goal of this project was to enhance the performance of a very basic CNN model provided by the professors, implementing 12 different experiments (in addition to the baseline model) to improve its accuracy.
- **Baseline Accuracy**: 60.6% on the test set.
- **Best Accuracy without Data Augmentation**: 87.7%.
- **Best Overall Accuracy**: 90.7% using Data Augmentation.
- **Transfer Learning**: The final experiment applied VGG-16, achieving lower accuracy than the augmented CNN. Future improvements will focus on better applying transfer learning.
## Key Skills
- **Deep Learning**: Implementation and optimization of CNN architectures.
- **Model Optimization**: Techniques like dropout, batch normalization, and data augmentation.
- **Data Augmentation**: Applied to improve model generalization and performance.
- **Transfer Learning**: Initial exploration using VGG-16, with plans to improve this aspect in future work.
- **Keras**: All models and experiments were implemented using the Keras library.
## Repository Structure
- `/notebooks`: Jupyter notebooks for each experiment conducted, detailing modifications and results.
- `/docs`: Final project report (PDF) with detailed explanations of each experiment, results, and future developments.
## Dataset
The CIFAR-10 dataset is available through [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/cifar10) or can be downloaded from the [official page](https://www.cs.toronto.edu/~kriz/cifar.html).
## Results and Future Work
The project successfully reached an accuracy of 90.7% using Data Augmentation. However, there is still room for improvement, especially in the area of **Transfer Learning**. The VGG-16 model used in the final experiment did not surpass the results obtained with Data Augmentation, and further study in this area could yield better results.
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**Feel free to reach out for any questions or further discussions!**
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## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.