Pysource Blog
In this tutorial, we will look at installing YOLO v8 on Mac M1, how to write the code from scratch, and how to run it on a video. We will also see how to manage the graphics card for the best possible performance. We will use YOLO v8 from ultralyticsc for object detection. Installation of […]
According to research on Computer vision syndrome (a.k.a. digital eye strain) by Mark Rosenfield of the State University of New York College of Optometry, ” Computer Vision Syndrome, also known as digital eye strain, is the set of eye and vision problems associated with computer use. ” For these reasons, especially if you are subjected […]
I built this people flow tracking prototype to track the flow of people in large areas such as airports, shopping malls, large open spaces, or whatever. In this video, we will see what the steps are to build this type of solution and what are the critical points for example object tracking in an uncontrolled […]
What is Yolo v8 segmentation for? In this tutorial, we will see how to use computer vision to apply segmentation to objects with Yolov8 by Ultralitycs. With the segmentation, the object’s shape is identified, allowing the calculation of its size. Another very popular and simple-to-use algorithm for object segmentation is mask r-CNN and also in […]
In this video, we will see how to build Real-time defect identification software for plastic bottles. Obviously, this is an operation that can be performed manually perhaps by taking samples of a production batch. Large companies already have sophisticated systems to check every single bottle, in this tutorial we will see how Real-time defect identification […]
In this video, we will see how to calculate the dimensions of a fixed camera object on a flat surface. For example, if we want to measure the sizes of the nuts we can use the diameter. Measuring the size of an object can be applied to anything, no matter what its outline, color, or […]
We will see how to apply flatting operation and dense layer into a convolutional neural network with Keras Flatten and Dense layers in a simple VGG16 architetture To better understand the concept and purpose of using Flatten and Dense layers let’s see this simple architecture of the VGG16 model as an example. There are several […]
We will see how to make the VGG16 model from scratch with Keras, I will enter all the steps until we arrive at the result. The VGG16 Neural Network is the result of a Very Deep Convolutional Neural Network for Large-Scale Image Recognition research by Karen Simonyan and Andrew Zisserman. The model achieves 92.7% and […]
In this tutorial, we will see what the Max pooling layer on a convolutional neural network is, what parameters to set and how to use it. Before going on with the lesson I suggest you, if you haven’t already done so, see the previous episode Feature map | Computer Vision with Keras p.4 What is […]
In this lesson, we will see in more detail what feature maps are, how to take them, and how to display them with Keras. Before going on I suggest you see the previous lesson Conv2D Layer | Computer Vision with Keras p.3 because what we get as a result of the convolutional layer is the […]
