A Guide to Global Pooling in Neural Networks
We explore what global average and max pooling entail. We discuss why they have come to be used and how they measure up against one another.
Pooling In Convolutional Neural Networks
Explore the whys and the hows behind the process of pooling in CNN architectures, and compare 2 common techniques: max and average pooling.
Training YOLOv8 on Custom Data
This blog post explores YOLOv8, comparing its architectural changes to YOLOv5. We’ll also demonstrate the new model’s Python API functionality by testing its detection capabilities on a Basketball dataset.
Attention Mechanisms in Computer Vision: CBAM
This post covers an in-depth analysis of the convolution block attention module (CBAM).
Automatic Hyperparameter Optimization With Keras Tuner
Learn how to utilize the search algorithms of Keras Tuner to automatically get the best hyperparameters for Tensorflow models.
Implementing Baidu’s RT-DETR for Real-Time Object Detection
In this tutorial, we explore Baidu’s RT-DETR object detection framework, explaining its implementation, features, and real-time performance. Learn how to use RT-DETR for deep learning, computer vision, and image recognition tasks with step-by-step guidance.
YOLOv9 Object Detection Features, Benefits, and Use Cases
Explore YOLOv9, known for the novel architecture GELAN and Reversible Network Architecture to address the unreliable gradient issue in Deep Neural Networks.
Capsule Networks: A Quick Primer
Introduction Whether you're new to deep learning or a serious researcher, you've surely encountered the term convolutional neural networks (CNNs). They are one of the most researched and top-performing architectures in the field. That being said, CNNs have a few drawbacks in recognizing features of input data when they are in different orientations. To address […]
Deep Learning for Image Dehazing- The What, Why, and How
In this tutorial, we discuss the history of image dehazing, show how to set an image dehazing task up in a notebook, and then examine 7 different techniques for performing image dehazing with deep learning!
SAM 2: Meta’s Next-Gen Model for Video and Image Segmentation
In this article, we will explore SAM 2, which expands the capabilities of the original SAM to handle both images and videos. It excels in real-time object segmentation, enabling dynamic interaction through prompts and memory attention.
