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CIFAR10 Image Classification using PyTorch Kaggle project
>> YOUR LINK HERE: ___ http://youtube.com/watch?v=uCdZVq1QH68
In this tutorial, we delve into a comprehensive deep learning project on image classification using PyTorch. We start by importing necessary libraries and dependencies, including Py7zr for handling compressed files and PyTorch for building and training our convolutional neural network (CNN) model. The dataset we use is the CIFAR-10 dataset, which consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. • The video covers: • Importing and preprocessing the CIFAR-10 dataset. • Splitting the dataset into training and validation sets. • Building and training a ResNet-18 CNN model for image classification. • Evaluating the model's performance using various metrics like accuracy, confusion matrix, precision-recall curves, and ROC curves. • Creating a submission file for test images. • This tutorial provides a step-by-step guide for beginners to understand how to implement a deep learning project for image classification using PyTorch. Whether you're new to deep learning or looking to expand your skills, this tutorial will help you grasp the fundamentals of building and training CNN models for image classification tasks. • Subscribe for more deep learning tutorials and don't forget to like and share this video if you find it helpful! • Download Code file - https://www.4shared.com/s/fUfHM2YeEge • Kaggle dataset - https://www.kaggle.com/competitions/c... • Kaggle Working Notebook - https://www.kaggle.com/code/manojajj/... • 💡 Support the Channel • If you enjoy my content and would like to support the channel, consider making a donation! Your contributions help me keep creating valuable videos and improving the quality of the content. • How You Can Donate: • UPI: (UPI id - 9380423907@axl) • Buy Me a Coffee: A simple way to support my work with a small contribution - https://buymeacoffee.com/manojajj • Every little bit helps, and I'm grateful for your support. Thank you! • #kaggle #cifar #pytorch #datascience #machinelearning
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