Prevent Your Model from Overfitting — Data augmentation is a technique to increase the diversity of dataset without an effort to collect any more real data but still help improve your model accuracy and prevent the model from overfitting. …

CSPNet (CSPDenseNet, CSPResNet & CSPResNeXt), Later on Used in YOLOv4 and Scaled-YOLOv4 — In this story, CSPNet: A New Backbone That Can Enhance Learning Capability of CNN, (CSPNet), by Institute of Information Science Academia Sinica, Elan Microelectronics Corporation, and National Chiao Tung University, is reviewed. In this paper: Cross Stage Partial Network (CSPNet) is designed, to attribute the problem to the duplicate gradient…

Designing Network Design Spaces A new SoTA network has been published, but in a very indiscreet way. It is understandable, since it is introduced in a paper that aims to further a more powerful idea. …

In this article, we are going to learn how to plot GradCam [1] in PyTorch. To get the GradCam outputs, we need the activation maps and the gradients of those activation maps. Let us jump straight into the code!! Imports import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import torch import…

The Inception network was an important milestone in the development of CNN classifiers. Prior to its inception (pun intended), most popular CNNs just stacked convolution layers deeper and deeper, hoping to get better performance. The Inception network on the other hand, was complex (heavily engineered). It used a lot of…

One of the hardest concepts to grasp when learning about Convolutional Neural Networks for object detection is the idea of anchor boxes. It is also one of the most important parameters you can tune for improved performance on your dataset. In fact, if anchor boxes are not tuned correctly, your…

Pelee = Proposed PeleeNet as Backbone + Modified SSD as Object Detection Network (Image Classification & Object Detection) — In this story, Pelee: A Real-Time Object Detection System on Mobile Devices, (Pelee & PeleeNet), by University of Western Ontario, is reviewed. In this paper: PeleeNet is proposed which use conventional convolution only, and it is a variant of DenseNet architecture for mobile devices.