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In this tutorial, you will learn how to train your own custom dlib shape predictor. You’ll then learn how to take your trained dlib shape predictor and use it to predict landmarks on input images and real-time video streams. Today…
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In this tutorial, you will learn to install TensorFlow 2.0 on your Ubuntu system either with or without a GPU. There are a number of important updates in TensorFlow 2.0, including eager execution, automatic differentiation, and better multi-GPU/distributed training support,…
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In this tutorial, you will learn to install TensorFlow 2.0 on your macOS system running either Catalina or Mojave There are a number of important updates in TensorFlow 2.0, including eager execution, automatic differentiation, and better multi-GPU/distributed training support, but…
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In this tutorial, you will learn how to use OpenCV and Deep Learning to detect vehicles in video streams, track them, and apply speed estimation to detect the MPH/KPH of the moving vehicle. This tutorial is inspired by PyImageSearch readers…
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In this tutorial you will learn how to perform Human Activity Recognition with OpenCV and Deep Learning. Our human activity recognition model can recognize over 400 activities with 78.4-94.5% accuracy (depending on the task). A sample of the activities can…
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In this tutorial, you will learn how to detect fire and smoke using Computer Vision, OpenCV, and the Keras Deep Learning library. Today’s tutorial is inspired by an email I received last week from PyImageSearch reader, Daniel. Daniel writes: Hey…
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In this tutorial, you will learn how to automatically detect natural disasters (earthquakes, floods, wildfires, cyclones/hurricanes) with up to 95% accuracy using Keras, Computer Vision, and Deep Learning. I remember the first time I ever experienced a natural disaster —…
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In this tutorial, you will learn how to train your own traffic sign classifier/recognizer capable of obtaining over 95% accuracy using Keras and Deep Learning. Last weekend I drove down to Maryland to visit my parents. As I pulled into…
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Keras and TensorFlow 2.0 provide you with three methods to implement your own neural network architectures: Sequential API Functional API Model subclassing Inside of this tutorial you’ll learn how to utilize each of these methods, including how to choose the…
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