https://github.com/abixnash/image-prediction-cifar
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.
https://github.com/abixnash/image-prediction-cifar
cifar-10-dataset cifar-10-image-prediction cifar10 cifar10-classification deep-learning internship internship-project jupyter-notebook python
Last synced: 3 months ago
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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.
- Host: GitHub
- URL: https://github.com/abixnash/image-prediction-cifar
- Owner: AbiXnash
- License: mit
- Created: 2023-10-02T17:57:58.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-10-02T19:06:51.000Z (almost 3 years ago)
- Last Synced: 2025-06-23T14:43:06.706Z (about 1 year ago)
- Topics: cifar-10-dataset, cifar-10-image-prediction, cifar10, cifar10-classification, deep-learning, internship, internship-project, jupyter-notebook, python
- Language: Jupyter Notebook
- Homepage:
- Size: 466 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# **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](LICENSE.md).
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.