WEBINAR: Deploying an Image Classification Model to the OpenMV Camera - 26 July 2022

 

Workshop Series Deploying an Image Classification Model to the OpenMV Camera Register Today Date: Tuesday, July 26, 2022 Time: 9:00 am PDT | 4 pm UTC | 7:00 pm EET Duration: 16 minutes In this IoT Central MicroSession with Edge Impulse, first see how the OpenMV camera is an all-in-one computer vision system that can be programmed with MicroPython. Then learn how to download a trained image classification model from Edge Impulse and deploy it to the OpenMV camera. You will use the OpenMV TensorFlow Lite library to perform inference and use the inference results to perform some action (e.g. write information on the preview screen when a particular object is seen). Instructor: Shawn Hymel, Senior Developer Relations Engineer, Edge Impulse Optional Hardware: OpenMV H7, OpenMV H7 R2, or OpenMV H7 Plus Preparation: Free sign-up at https://studio.edgeimpulse.com/signup Register Today

Lossless Image Compression through Super-Resolution by Sheng "Scott" Cao via @github

This is the official implementation of SReC in PyTorch. SReC frames lossless compression as a super-resolution problem and applies neural networks to compress images. SReC can achieve state-of-the-art compression rates on large datasets with practical runtimes. Training, compression, and decompression are fully supported and open-sourced.

This is really interesting and very useful. The link is to a paper in Github.

Google has released a tool to spot faked and doctored images by Karen Hao via @techreview

Google has released a tool to spot faked and doctored images.

Very much needed - I really hope it works and is adopted widely.

How Etsy taught style to an algorithm by/via @FastCompany

Is it romantic or rustic? Boho or minimal? Etsy needed to offer searchers a way to find goods that matched their style aesthetics, but since descriptions aren’t uniform and don’t always describe the style, text mining the descriptions wasn’t enough. Colour and patterns don’t reliably predict style, so image recognition alone didn’t do it either. Enter a model that blends text analysis with image recognition based on 43 human-identified styles.

I love this real-life example detailing the steps they took to work out how to do this. Definitely, a methodology that could be used by other organisations to do a similar type of thing.

The Foundations of Algorithmic Bias by Zachary Chase Lipton via @kdnuggets

We might hope that algorithmic decision making would be free of biases. But increasingly, the public is starting to realise that machine learning systems can exhibit these same biases and more. In this post, we look at precisely how that happens.

Please note this is a three page long post.

I really like this article and it really makes you think as you read through it - definitely recommended reading.

How Convolutional Neural Networks Work by Brandon Rohrer via @kdnuggets

Get an overview of what is going on inside convolutional neural networks, and what it is that makes them so effective.

This is very clear and easy to understand. It is a two page article.