Welcome to Part 2 of our blog series on Adversarial Machine Learning! In this installment, we will explore the Fast Gradient Sign Method (FGSM), one of the most widely used and effective adversarial attack techniques. We will understand how the attack works, its significance, and implement the attack through PyTorch code.Read More
In this blog post, we will discuss how to fine-tune a pre-trained deep learning model using PyTorch. Fine-tuning is a powerful technique that allows us to leverage the knowledge learned by a pre-trained model on a large dataset and apply it to a new task. This can save a significant amount of time and resources compared to training a model from scratch. The fine-tuned model achieved 92.34% accuracy on the test set.Read More
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.Read More