Skip to content

This GitHub repository hosts my comprehensive CIFAR-10 image prediction project, which I completed as part of the SmartKnower program. CIFAR-10 is a widely used dataset in computer vision, consisting of 60,000 32x32 color images from 10 different classes.

License

Notifications You must be signed in to change notification settings

AbiXnash/Image-Prediction-CIFAR

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

15 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Project Title: CIFAR-10 Image Prediction

Description:

This GitHub repository hosts my comprehensive CIFAR-10 image prediction project, which I completed as part of the SmartKnower program. CIFAR-10 is a widely used dataset in computer vision, consisting of 60,000 32x32 color images from 10 different classes. The primary goal of this project was to develop a deep learning model capable of accurately classifying these images into their respective categories.

Key Features and Highlights:

  1. Deep Learning Models: I've implemented and fine-tuned several deep learning architectures, including Convolutional Neural Networks (CNNs), to achieve high accuracy on the CIFAR-10 dataset.

  2. Data Preprocessing: Extensive data preprocessing techniques were applied to enhance the model's performance, including data augmentation, normalization, and one-hot encoding of labels.

  3. Model Evaluation: I have thoroughly evaluated the models using various performance metrics like accuracy, precision, recall, and F1-score to provide a comprehensive assessment of their capabilities.

  4. Visualization: The repository includes visualization tools to display sample images, model predictions, and training/validation curves, allowing for a better understanding of model behavior.

  5. Model Checkpoints: Checkpoints of the trained models are provided, making it easy to reproduce results and continue training if necessary.

  6. Jupyter Notebooks: You can find Jupyter notebooks with detailed explanations of the project's steps, making it accessible for learning and understanding.

  7. Dependencies: A list of required libraries and dependencies is provided to help set up the project environment.

Usage: Feel free to clone or fork this repository to explore, modify, or use the code for your own projects. If you find it helpful, don't forget to star the repository!

Contributions and Issues: Contributions and suggestions are welcome! If you encounter any issues or have ideas for improvements, please open an issue or submit a pull request.

Acknowledgments: I would like to express my gratitude to SmartKnower for providing the knowledge and resources that enabled me to complete this project successfully.

License: This project is licensed under the MIT License.

Thank you for visiting my CIFAR-10 Image Prediction project repository. I hope you find it informative and inspiring for your own machine learning and computer vision endeavors.

About

This GitHub repository hosts my comprehensive CIFAR-10 image prediction project, which I completed as part of the SmartKnower program. CIFAR-10 is a widely used dataset in computer vision, consisting of 60,000 32x32 color images from 10 different classes.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published