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Mobile App for platform.ai users

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Atin Matur
September 15, 2020

The idea behind the platform.ai mobile app is to help users test their deep learning models in real-time using their smartphones. After creating a new project on platform.ai, users can use their smartphone camera or gallery to select a picture and classify it using their own deep learning models on platform.ai.

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Latest posts

Predicting Rich Attributes in Real Estate Images

Visual attribute search can greatly improve the user experience and SEO for home listing and travel websites. Although Zillow, Redfin, Airbnb, and TripAdvisor already have some metadata about the amenities of a property, they can expand searchable attributes by analyzing the property images using vision models.

David Samuel, Naveen Kumar, Atin Mathur, Arshak Navruzyan
October 6, 2020
Tutorial

The Silhouette Loss Function: Metric Learning with a Cluster Validity Index

At its heart, platform.ai is all about visualisation. We aim to take any large dataset of unlabelled images and place each image at a position on the screen, such that similar images are positioned closer together. In this way, we can help users label vast numbers of images with minimal effort.

October 6, 2020

Attention Cropping in platform.ai

One way to make categorical image labelling easier on platform.ai is to zoom in on the objects of interest, showing the user images with irrelevant background cropped away. This could generate a large improvement in ease of use: in some datasets the relevant objects only occupy a small area, and recognizing them is often tricky. Further, showing users a combination of regular and zoomed images could allow them to recognize a wider range of patterns.

Krisztian Kovacs, Constantin Baumgartner, Niko Laskaris, Richard Lipkin PhD
October 6, 2020

New State-of-the-art in Logo Detection Using YOLOv3 and Darknet

Logo detection in images has many applications, such as brand recognition for marketing analytics and intellectual property protection. As a computer vision problem logo detection is challenging for two primary reasons: there is no fixed number of company logos to classify, and in wild images the logo itself is often a very small feature of the input image. State-of-the-art (SOTA) results have been achieved using object localization methods.

Niko Laskaris and Aradhya Chouhan
October 6, 2020

Classifying Burn Depth

The American Burn Association reports that roughly 485,000 patients receive hospital and emergency room treatment for burns each year [1]. Proper burn treatment depends primarily on the depth of tissue damage. Although accurate burn-depth assessment is the first and arguably most important step in a patient’s recovery, burn depth is still misdiagnosed 20%–40% of the time, even among experienced practitioners

by Constantin Baumgartner, Richard Lipkin, and Dr. Peter Grossman, MD
October 6, 2020

Surpassing Human Judgement for Fashion Style

We analyse how machines distinguish fashion styles. In so doing, we surpass the previous state of the art performance in classifying fashion styles and demonstrate that computer vision algorithms can significantly outperform human users on sensing style. Our solution is only surpassed by a selected group of "savvy" users.

Baptiste Metge, David Pollack and Krisztian Kovacs
October 6, 2020

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