Multiclass Image Segmentation in PyTorch | U-Net Tutorial

In this video, we’ll walk through the implementation of multiclass image segmentation using the U-Net architecture in PyTorch, applied to real-world agricultural weed datasets. This tutorial demonstrates how deep learning can be leveraged for precision agriculture by accurately identifying and classifying various weed types. 🔗 Source Code: 👉 https://github.com/nikhilroxtomar/Multiclass-Segmentation-in-PyTorch ⏱️ Timestamps: 00:00 - Introduction 00:10 - Dataset Overview (Weed Segmentation) 01:00 - U-Net Architecture Explained 02:52 - Utility Functions Implementation 06:27 - Loss Functions 06:42 - Model Training 21:29 - Testing and Inference 37:42 - Final Thoughts & Wrap-up 📚 What You’ll Learn: ✅ What is Multiclass Image Segmentation ✅ How to Train a U-Net Model in PyTorch ✅ Testing on Real Agricultural Data ✅ Evaluating Multiclass Segmentation Performance 💖 Support My Work: ☕ Buy me a coffee: https://www.buymeacoffee.com/nikhilroxtomar 💬 Join the channel as a member: https://www.youtube.com/channel/UClkqp31PHke-f8b8mjiiY-Q/join 🌐 Stay Connected: 📘 Blog: idiotdeveloper.com | sciencetonight.com 📢 Telegram: https://t.me/idiotdeveloper 📘 Facebook: https://www.facebook.com/idiotdeveloper 🐦 Twitter: https://twitter.com/nikhilroxtomar 📸 Instagram: https://instagram.com/nikhilroxtomar 🎁 Patreon: https://www.patreon.com/idiotdeveloper 👍 If you find this tutorial helpful, like, subscribe, and drop a comment below with your thoughts or questions! Your support helps me keep creating high-quality content ❤️