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In-Browser object detection using YOLO and TensorFlow.js

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ffff  ome time ago, I spent several evenings playing around with state of the art object detection model called YOLO, which is certainly known to those who are interested in Machine Learning on a daily basis. Originally written in Darknet  — open source neural network framework — YOLO performs really well in the tasks of locating and recognizing objects on the pictures. Due to the fact that I have been interested in TensorFlow.js for a few weeks now, I decided to check how YOLO will handle the limitations of In-Browser computing. The entire source code as well as my previous TF.js projects can be found on GitHub . If you want to play with the demo version, visit “I Learn Machne Learning” project website . Note: I also recommend reading an article that recently appeared on Towards Data Science, which was the starting point for my project. I will try not to duplicate the information contained in it but rather broaden the topics raised in it and hopefully enri...
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Object detection and tracking in PyTorch

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Detecting multiple objects in images and tracking them in videos Chris Fotache Dec 10 In my previous story, I went over how to train an image classifier in PyTorch , with your own images, and then use it for image recognition. Now I’ll show you how to use a pre-trained classifier to detect multiple objects in an image, and later track them across a video. What’s the difference between image classification (recognition) and object detection? In classification, you identify what’s the main object in the image and the entire image is classified by a single class. In detection, multiple objects are identified in the image, classified, and a location is also determined (as a bounding box). Object Detection in Images There are several algorithms for object detection, with YOLO and SSD among the most popular. For this story, I’ll use YOLOv3 . I won’t get into the technical details of how YOLO (You Only Look Once) works — you can read that here  — but...
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Analyzing my Google Location History

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I recently read this article on how to create a heat-map from your Google Location History data. Trying it out myself, I got some amazing results: The large red circles represent the cities I have spent a significant amount of time. The purple shade in different places represents places I had travelled to or passed through while on a train journey. My previous phone had some GPS issues, which was leading to my location being shown in Arizona, USA!. Surprisingly (or not?) it even gave a proof of that! All this was really cool to look at, but I really wanted to dive in and get some more insights into my travelling patterns throughout the years. Data Pre-Processing Like most data science problems, data pre-processing was definitely the pain point. The data was in a JSON format where the meaning of different attributes wasn’t very clear. Data Extraction {'timestampMs': '1541235389345', 'latitudeE7': 286648226, 'longitudeE7...
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