Deep learning models, especially convolutional neural networks (CNNs), often function as black boxes, making it difficult to interpret their decision-making processes. Gradient-weighted Class Activation Mapping (GradCAM) is a powerful technique used to visualize and understand these models by highlighting the regions of an image that contribute most to a prediction. Continue Reading
opencv
GradCAM with TensorFlow: Interpreting Neural Networks with Class Activation Maps
Deep learning models, particularly convolutional neural networks (CNNs), are widely used for image classification, object detection, and various computer vision tasks. However, these models are often referred to as “black boxes” due to their complex decision-making processes. To interpret these decisions and understand what parts of an image influence the Continue Reading
Read Video Files Using OpenCV Python
Reading and processing video files is a common task in computer vision, and OpenCV makes it easy to work with video data. In this article, we’ll go through the process of reading and displaying video files using OpenCV. Whether you’re working on a video analysis project or want to learn Continue Reading
Image Masking with OpenCV AddWeighted
Image masking is a powerful technique used in image processing to manipulate specific parts of an image while leaving other areas untouched. This is particularly useful in applications like object detection, image segmentation, and photo editing. In this tutorial, we’ll explore how to perform image masking using OpenCV addWeighted function. Continue Reading
ResUNET: A TensorFlow Implementation for Semantic Segmentation
In computer vision and medical image analysis, semantic segmentation plays a pivotal role in understanding and interpreting visual data. One of the prominent architectures in this domain is ResUNet, a fusion of U-Net and ResNet architectures, renowned for its ability to efficiently capture local and global features. In this blog Continue Reading
Image Segmentation-based Background Removal in TensorFlow
Image segmentation is an important area of computer vision that involves dividing an image into multiple segments, each of which corresponds to a different object. Background removal is one of the crucial applications of image segmentation that involves separating foreground objects from the background. This can be useful in various Continue Reading
Exploring Bounding Boxes and Different Annotation Formats in Object Detection
Bounding boxes play a critical role in the object detection process, a fundamental task in computer vision. Object detection entails the identification and precise localization of objects within an image. These bounding boxes serve to define the position of an object within the image, allowing for the creation of a Continue Reading
What is Intersection over Union (IoU) in Object Detection?
Intersection over Union (IoU) is a popular evaluation metric used in the field of computer vision and object detection. It is used to calculate the overlap between two bounding boxes and is used to evaluate the accuracy of object detection algorithms. IoU is a value between 0 and 1 that Continue Reading
Human Face Detection using Multi-task Cascaded Convolutional Networks in TensorFlow
In recent years, advances in machine learning and deep learning techniques have revolutionized the field of computer vision. With the help of these techniques, we can now detect and identify objects in real time with remarkable accuracy. One of the most popular tasks in computer vision is human face detection, Continue Reading
What is U2-Net or U-square Net
U2-Net is a simple and powerful architecture designed for the purpose of salient object detection (SOD). It is a two-level nested U-shaped architecture built using the proposed ReSidual U-blocks (RSU). The U2-Net does not use any pre-trained architecture and is trained from scratch. The architecture comes with two variants: U2-Net Continue Reading