Computer Vision

February 1, 2022

Summary of AlexNet

Thoviti Siddharth Machine Learning/Artificial Intelligence Alexnet, Alexnet-Summary, CNN, Computer Vision, DCNN, Dropout, ImageNet, Local Response Normalization, Machine Learning, Overfitting, Overlapping Pooling, ReLU, Summary, TanH 1

Title: ImageNet Classification with Deep Convolutional Neural Networks (2012)

Authors: Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

Point of the Paper: “GPU memory enables training large networks on large image datasets.”

At a time when human performance was achieved on relatively small image datasets like MNIST and CIFAR-10, computer vision researchers were eager to achieve the same with large, high-resolution image datasets. Although datasets such as ImageNet provided large and real-world data, simple convolution neural networks (CNN) did not perform well on such high-resolution datasets. Innovative trials were limited by computational resources. AlexNet solved these problems by introducing deep CNN that achieved the state-of-the-art in the ImageNet LSVRC-2010 contest, and GPU parallelization.

December 10, 2020

Traffic Sign Detection with Faster R-CNN

Thoviti Siddharth Machine Learning/Artificial Intelligence Computer Vision, Faster R-CNN, Finetuning, Machine Learning, Machine Vision, Object Detection, PyTorch 3

Autonomous vehicles that are capable of navigating without human inputs are heavily dependent on computer vision tasks such as object detection. Many object detection algorithms such as YOLO, Faster R-CNN, SSD, have been proposed to detect and recognize the traffic signs.

In this blog, Faster R-CNN will be used for the detection of German traffic signs (GTSDB dataset) with reference to Torchvision’s object detection finetuning tutorial.