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README.md

Computer Vision

48 articles on computer vision — convolutional networks, object detection, image segmentation, and transfer learning.

Part of the xbe.at knowledge base. ← Back to index


What's inside

CNN fundamentals

  • Convolution operations: kernels, padding, stride, pooling
  • MaxPooling, average pooling, global pooling
  • Batch normalization in CNNs, dropout for regularization
  • Preventing overfitting in convolutional networks

Architectures

  • Classic nets: VGG, AlexNet, ZFNet, Inception, ResNet
  • Vision Transformer (ViT)
  • Implementing and comparing architectures on CIFAR-10, ImageNet

Transfer learning

  • Fine-tuning pre-trained models (VGG, ResNet, and others)
  • 7 transfer learning models for CNNs
  • Domain adaptation for computer vision

Object detection & segmentation

  • YOLO object detection with Python
  • Image segmentation with K-Means, SAM, MASA
  • Deep Dive into SAM (Segment Anything Model)
  • Hidden flaws in object detection models

Explainability & visualization

  • Grad-CAM: visualizing CNN decision-making
  • Visualizing batch normalization's impact on CNN evolution
  • Intuition behind convolution and pooling

Other

  • 3D graphics fundamentals, monocular depth estimation
  • Face recognition, face generation (Active Shape Model)
  • ECG analysis with CNNs and transfer learning

Highlights

  • Mastering YOLO Object Detection with Python
  • Visualizing CNN Decision-Making with Grad-CAM
  • Deep Dive into Image Segmentation with SAM
  • Deep Residual Learning for Image Recognition in Python
  • ImageNet Classification with Deep Convolutional Neural Networks