Explores how images are discretized into pixels, the impact of sampling grids on deep learning models, and inconsistencies in image processing libraries.
A technical tutorial explaining the fundamentals of Convolutional Neural Networks (CNNs) by manually calculating layers from the classic LeNet-5 architecture.
An overview of tools and techniques for creating clear and insightful diagrams to visualize complex neural network architectures.
Explores the R-CNN family of models for object detection, covering R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN with technical details.
Explores classic CNN architectures for image classification, including AlexNet, VGG, and ResNet, as foundational models for object detection.
Explains the three key research papers behind Facebook's computer vision pipeline for object segmentation: DeepMask, SharpMask, and MultiPathNet.
