This repository contains a comprehensive tutorial for Computer Vision tasks using PyTorch. The tutorial focuses on the fundamentals of deep learning models and their application to visual data. With clear explanations and code examples, you'll learn how to build, train, and evaluate computer vision models using PyTorch.
- Building CNN (Convolutional Neural Networks) from scratch
- Image classification using pre-trained models (e.g., ResNet, VGG)
- Data augmentation techniques for improving model performance
- Transfer learning for fine-tuning pre-trained models
- Evaluation metrics for classification tasks (accuracy, confusion matrix, etc.)
- Deploying models for inference and prediction
This tutorial is perfect for beginners looking to get started with deep learning and computer vision using PyTorch, as well as those interested in refining their skills with practical applications.
- Python
- PyTorch
- NumPy, Matplotlib
- torchvision