Computer Vision

Learn the fundamentals of computer vision, understanding how machines interpret visual data, the CV pipeline, and setting up your development environment with OpenCV and PyTorch.

Master essential image processing techniques including filtering operations, edge detection algorithms, morphological transformations, and color space conversions.

Master traditional computer vision feature detection techniques including SIFT, SURF, ORB, and HOG descriptors for image matching, recognition, and analysis.

Understand the architecture and mechanics of CNNs, from convolution operations and pooling layers to modern architectures like VGG, ResNet, and EfficientNet.

Build complete image classification systems using PyTorch, implementing data augmentation, transfer learning, and fine-tuning pretrained models for custom datasets.

Master object detection techniques including YOLO, SSD, and Faster R-CNN, understanding anchor boxes, non-maximum suppression, and evaluation metrics like mAP.

Master pixel-wise image classification with FCN, U-Net, and DeepLab architectures for dense prediction tasks.

Master instance segmentation techniques including Mask R-CNN, panoptic segmentation, and evaluation metrics for pixel-level object detection.

Explore Generative Adversarial Networks for image synthesis, from basic GANs to StyleGAN and image-to-image translation with pix2pix.

Master transfer learning techniques using pretrained models, from feature extraction to fine-tuning strategies and domain adaptation.

Master video processing fundamentals including optical flow, object tracking with SORT and DeepSORT, and action recognition techniques.

Explore 3D vision techniques including depth estimation, stereo vision, point cloud processing, camera calibration, and 3D reconstruction.