Head on over to Hacker Noon for an exploration of doing image classification at lightning speed using the relatively new MobileNet architecture. We classify images at 450 images per second!

The post covers the following:

  • How to build a custom dataset to train a MobileNet with TensorFlow
  • How to train a MobileNet that’s pretrained on ImageNet with TensorFlow
  • How MobileNets perform against Inception V3
  • How to use your retrained MobileNet to classify images

Read the post at Hacker Noon.


Part two of a two-part series: It’s like hot dog not hot dog, but for roads

In part 1, Creating Insanely Fast Image Classifiers with MobileNet in TensorFlow, we covered how to retrain a MobileNet on a new dataset. Specifically, we trained a classifier to detect Road or Not Road at more than 400 frames per second on a laptop.

MobileNets are made for — wait…


“It’s like hot dog not hot dog, but for roads.”

MobileNets are a new family of convolutional neural networks that are set to blow your mind, and today we’re going to train one on a custom dataset.

There are a few things that make MobileNets awesome:

  1. They’re insanely fast
  2. They’re remarkably accurate
  3. They’re easy to tune for…


One of the challenges of hyperparameter tuning a deep neural network is the time it takes to train and evaluate each set of parameters. If you’re anything like me, you often have four or five networks in mind that you want to try: different depth, different units per layer, etc.


Building the perfect deep learning network involves a hefty amount of art to accompany sound science. …


Exploring the UCF101 video action dataset

[h/t @joshumaule and @surlyrightclick for the epic artwork.]

Classifying video presents unique challenges for machine learning models. As I’ve covered in my previous posts, video has the added (and interesting) property of temporal features in addition to the spatial features present in 2D images. …


The magical power of deep learning in 2017.

Deep learning in 2017 is magical. We get to apply immensely complex algorithms to equally complex problems without having to spend all our time writing the algorithms ourselves. …


Part 2 of a series exploring continuous classification methods.

A video is a sequence of images. In our previous post, we explored a method for continuous online video classification that treated each frame as discrete, as if its context relative to previous frames was unimportant. Today, we’re going to stop treating our video as individual photos and start treating…


Or, using convolutional neural networks to identify what’s on TV

Much has been written about using deep learning to classify prerecorded video clips. These papers and projects impressive tag, classify and even caption each clip, with each comprising a single action or subject.

Today, we’re going to explore a way to continuously classify video as it’s captured, in an online…


If you’re like me, you often find yourself wanting to send a notification that some running task has completed, or that some process needs attention.

The way I’ve done this for years is to send myself an e-mail. But because I’m a terrible sys admin I find it a pain…

Matt Harvey

Founder of Coastline Automation, using AI to make every car crash-proof.

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