L1_02_CIFAR_10_Image_Classification

L1 - 02: Image Classification using ConvNets on CIFAR 10

Overview

Using Convolutional Neural Networks (CNNs) to classify images of different types of objects from the CIFAR-10 dataset.

Requirements:

- Python
- TensorFlow
- PyTorch
- CIFAR-10 dataset
- Google Colab for running the code

Implementation Steps:

1. Load and Explore the CIFAR-10 Dataset
2. Preprocess the Data
3. Build the CNN Model
4. Compile the Model
5. Train the Model
6. Evaluate the Model
7. Make Predictions

Usage

Please run the notebook to see the results.

Contributing

If you want to contribute to this project, you are welcome to do so. You can either add new projects, improve existing ones, or fix bugs and errors.

Please follow these steps to contribute:

• Fork this repository and clone it to your local machine.
• Create a new branch with a descriptive name for your contribution.
• Add your code and files to the branch and commit your changes.
• Push your branch to your forked repository and create a pull request to the main repository.
• Wait for your pull request to be reviewed and merged.

References

• CIFAR-10 - Wikipedia
• CIFAR-10 (Canadian Institute for Advanced Research, 10 classes) - paperwithcode.com
• Cifar10 datasets - Huggingface