📚 Introduction

Image segmentation is a critical task in computer vision, enabling machines to understand and interpret visual data at a pixel level. This tutorial explores how deep learning models like U-Net and FCN (Fully Convolutional Network) revolutionize this field.

🔧 Key Concepts

  • Pixel-wise classification: Assigning a class label to each pixel in an image 📊
  • Loss functions: Dice coefficient or cross-entropy for training 📉
  • Data augmentation: Enhancing model generalization with rotations, flips, etc. 🔄

🧩 Popular Models

  1. U-Net

    • Encoder-decoder architecture with skip connections
    • Widely used in medical imaging 🩺
    U_Net

  2. FCN

    • Replaces fully connected layers with convolutional layers
    • Enables dense predictions for segmentation 🧾
    Fully_Convolutional_Network

🛠️ Practical Steps

  1. Data Preparation

    • Use labeled datasets (e.g., COCO, Cityscapes) 📁
    • Preprocess images and masks with normalization and resizing 📏

  2. Model Training

    • Implement loss functions and optimization techniques 🚀
    • Monitor performance with metrics like IoU (Intersection over Union) 📈

  3. Inference & Post-processing

    • Apply the trained model to new images 📸
    • Use techniques like thresholding or morphological operations to refine results 🧹

🌐 Resources

Deep_Learning_Segmentation