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 who have emailed me asking for speed estimation computer vision solutions. As pedestrians taking the […]
Human Activity Recognition with OpenCV and Deep Learning
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 be seen below: archery arm wrestling baking cookies counting money driving tractor eating hotdog flying […]
An interview with Paul Lee – Doctor, Cardiologist and Deep Learning Researcher
In today’s blog post, I interview Dr. Paul Lee, a PyImageSearch reader and interventional cardiologist affiliated with NY Mount Sinai School of Medicine. Dr. Lee recently presented his research at the prestigious American Heart Association Scientific Session in Philadelphia, PA where he demonstrated how Convolutional Neural Networks can: Automatically analyze and interpret coronary angiograms Detect […]
Fire and smoke detection with Keras and Deep Learning
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 Adrian, I’m not sure if you’ve seen the news, but my home state of California […]
Detecting Natural Disasters with Keras and Deep Learning
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 — I was just a kid in kindergarten, no more than 6-7 years old. We were […]
Traffic Sign Classification with Keras and Deep Learning
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 their driveway I noticed something strange — there was a car I didn’t recognize sitting […]
3 ways to create a Keras model with TensorFlow 2.0 (Sequential, Functional, and Model Subclassing)
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 right API for the job. To learn more about Sequential, Functional, and Model subclassing with […]
Keras vs. tf.keras: What’s the difference in TensorFlow 2.0?
In this tutorial you’ll discover the difference between Keras and tf.keras , including what’s new in TensorFlow 2.0. Today’s tutorial is inspired from an email I received last Tuesday from PyImageSearch reader, Jeremiah. Jeremiah asks: Hi Adrian, I saw that TensorFlow 2.0 was released a few days ago. TensorFlow developers seem to be promoting Keras, or […]
Why is my validation loss lower than my training loss?
In this tutorial, you will learn the three primary reasons your validation loss may be lower than your training loss when training your own custom deep neural networks. I first became interested in studying machine learning and neural networks in late high school. Back then there weren’t many accessible machine learning libraries — and there […]
Is Rectified Adam actually *better* than Adam?
Is the Rectified Adam (RAdam) optimizer actually better than the standard Adam optimizer? According to my 24 experiments, the answer is no, typically not (but there are cases where you do want to use it instead of Adam). In Liu et al.’s 2018 paper, On the Variance of the Adaptive Learning Rate and Beyond, the […]