This repository contains a Jupyter Notebook for classifying images from the CIFAR10 dataset using a convolutional neural network (CNN). The notebook is designed to be a comprehensive guide, covering everything from setting up your environment, data preprocessing, model creation, training, and evaluation.
To get started, clone this repository to your local machine or Google Colab environment.
git clone https://github.com/pramodyasahan/PyTorchCIFAR10.gitBefore running the notebook, ensure you have the following dependencies installed:
- torch
- torchvision
- tqdm
- matplotlib
You can install these dependencies using pip:
pip install torch torchvision tqdm matplotlib-
Set Up Your Environment: Ensure that you are running the notebook in an environment where the dependencies listed above are installed. If you're using Google Colab, these dependencies are already available.
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Import Necessary Dependencies: The notebook starts by importing all the necessary libraries, including
torch,torchvision,tqdm, andmatplotlib. -
Device Configuration: Make sure to configure the device for training. The notebook is set up to use CUDA if available; otherwise, it falls back to CPU.
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Data Preprocessing: The notebook provides detailed steps for data preprocessing, including defining transforms for training and test data.
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Model Training and Evaluation: Follow the steps in the notebook to train and evaluate your CIFAR10 classifier.
If you encounter any issues or have questions, please open an issue in this GitHub repository.