Modern Computer Vision with PyTorch, 2E Code Repository

This repository contains code examples for the "Modern Computer Vision with PyTorch, Second Edition" book, targeting beginners and experienced professionals. It provides a practical guide to implementing state-of-the-art computer vision techniques using PyTorch, covering fundamentals through to advanced applications and generative AI, enabling users to build and deploy CV solutions.

How It Works

The project utilizes PyTorch to demonstrate a comprehensive suite of computer vision models and techniques. It progresses from basic neural networks and Convolutional Neural Networks (CNNs) to advanced architectures like Vision Transformers (ViT), CLIP, and diffusion models. The examples cover essential aspects such as data preprocessing, hyperparameter tuning, model training, and practical deployment strategies.

Quick Start & Requirements

• Software: Python 3.6 and above, PyTorch 1.7. Google Colab is supported.
• Hardware: Minimum 8 GB RAM, Intel i5 processor or better, NVIDIA 8 GB+ graphics card (GTX1070 or better recommended).
• Network: Minimum 50 Mbps internet speed.
• OS: Windows, macOS, Linux (Any).
• Setup: Primarily intended for use with the book's examples, likely involving cloning the repository and running provided Jupyter notebooks.

Highlighted Details

• Covers multimodal models like CLIP, BLIP2, and transformer-based architectures (ViT, TrOCR, LayoutLM).
• Includes practical examples for generative AI, such as Stable Diffusion, GANs (DCGAN, StyleGAN2), and image manipulation (inpainting, super-resolution).
• Demonstrates object detection (YOLOv8, Faster R-CNN, SSD) and image segmentation (U-Net, SAM).
• Features sections on combining CV with NLP for tasks like OCR and visual question-answering, and on moving models to production (ONNX, quantization).

Maintenance & Community

This repository serves as code support for a published book. No specific community channels (e.g., Discord, Slack) or active maintenance details beyond the book's publication are provided. Issues regarding missing datasets are directed to a Hugging Face link.

Licensing & Compatibility

The provided README does not specify a software license for the code. Users should exercise caution regarding usage rights and compatibility, especially for commercial applications.

Limitations & Caveats

Some datasets referenced in the book may be unavailable from their original sources; users are advised to check the provided Hugging Face link for alternatives. The code is designed as educational examples and may require adaptation for direct production deployment.