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 […]
Rectified Adam (RAdam) optimizer with Keras
In this tutorial, you will learn how to use Keras and the Rectified Adam optimizer as a drop-in replacement for the standard Adam optimizer, potentially leading to a higher accuracy model (and in fewer epochs). Today we’re kicking off a two-part series on the Rectified Adam optimizer: Rectified Adam (RAdam) optimizer with Keras (today’s post) […]
Keras: Starting, stopping, and resuming training
In this tutorial, you will learn how to use Keras to train a neural network, stop training, update your learning rate, and then resume training from where you left off using the new learning rate. Using this method you can increase your accuracy while decreasing model loss. Today’s tutorial is inspired by a question I […]
Install OpenCV 4 on Raspberry Pi 4 and Raspbian Buster
In this tutorial you will learn how to install OpenCV 4 on the Raspberry Pi 4 and Raspbian Buster. You will learn how to install OpenCV 4 on Raspbian Buster via both: A simple pip-install method (which can be completed in a matter of minutes) Compiling from source (which will take longer but will give you […]
Multiprocessing with OpenCV and Python
By Adrian Rosebrock on September 9, 2019 in Image Descriptors, Image Search Engine Basics, Tutorials
In this tutorial, you will learn how to use multiprocessing with OpenCV and Python to perform feature extraction. You’ll learn how to use multiprocessing with OpenCV to parallelize feature extraction across the system bus, including all processors and cores on your computer. Today’s tutorial is inspired by PyImageSearch reader, Abigail. Abigail writes: Hey Adrian, I […]