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Dwell Time Analysis with Computer Vision | Real-Time Stream Processing

Learn how to use computer vision to analyze wait times and optimize processes. This tutorial covers object detection, tracking, and calculating time spent in designated zones. Use these techniques to improve customer experience in retail, traffic management, or other scenarios.

Speed Estimation & Vehicle Tracking | Computer Vision | Open Source

Learn how to track and estimate the speed of vehicles using YOLO, ByteTrack, and Roboflow Inference. This comprehensive tutorial covers object detection, multi-object tracking, filtering detections, perspective transformation, speed estimation, visualization improvements, and more.

Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking

In this video, we explore real-time traffic analysis using YOLOv8 and ByteTrack to detect and track vehicles on aerial images. Harnessing the power of Python and Supervision, we delve deep into assigning cars to specific entry zones and understanding their direction of movement.

SAM - Segment Anything Model by Meta AI: Complete Guide

Discover the incredible potential of Meta AI's Segment Anything Model (SAM)! We dive into SAM, an efficient and promptable model for image segmentation, which has revolutionized computer vision tasks.

make sense	yolov5.js
Free to use online tool for labelling photos in small Computer Vision projects.	Effortless YOLOv5 JavaScript deployment. Enrich your website with Computer Vision.

Deep Dive into Math Behind Deep Networks

Nowadays, having at our disposal many high-level, specialized libraries and frameworks such as Keras, TensorFlow or PyTorch, we do not need to constantly worry about the size of our weights matrices or remembers formula for the derivative of activation function we decided to use. Often all we need to create a neural network, even one with a very complicated structure, is a few imports and a few lines of code...

Let’s code a Neural Network in plain NumPy

Not so long ago I published an article, explaining — in a simple way — how neural nets work. However, it was highly theoretical post, dedicated primarily to math, which is the source of NN superpower. From the beginning I was planning to follow-up this topic in a more practical way. This time we will try to utilize our knowledge and build a fully operational neural network using only NumPy...