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Author: torontoai

Capturing memories: GeoSnapShot uses Amazon Rekognition to identify athletes

Written on . Posted in Amazon.

If you’ve ever competed in a sporting event and painstakingly sifted through event photos to find yourself later, you’ll appreciate GeoSnapShot’s innovative solution powered by Amazon Rekognition.

GeoSnapShot founder Andy Edwards was first introduced to the world of sports photography when he started accompanying his wife, a competitive equestrian, to her riding events and photographing her and her friends.While he enjoyed taking great photos of everyone, he was frustrated by the manual, time-intensive process required post-event to identify each rider and distribute the photos to them. He noticed many other photographers in the same situation, and the sad consequence was a loss of the special memories they captured simply because the sorting process was too hard.

Setting out to solve this challenge – and indeed, multiple related challenges for photographers and sports organizations worldwide – Andy started GeoSnapShot in 2013. The company partners with event organizers to enable any athlete who opts in and uploads a selfie to find images of themselves quickly and easily. It does this by using Amazon Rekognition in two ways: for direct comparisons of users’ selfies to the photos from an event, and for optical character recognition to identify their competition bib numbers. With those inputs, GeoSnapShot is able to process thousands of event photos in near real-time, expediting an effort that used to require event organizers to spend many hours manually matching bib numbers to athlete names and sorting the photos by athlete.

This heavy lifting meant that athletes used to wait days or weeks for their photos to be available. Now, GeoSnapShot’s unique solution for sports photography makes it possible for athletes to start reviewing their photos before the sweat has even dried. As a result, photography sales for event organizers have increased by almost 30 percent, and customer satisfaction has increased substantially.

GeoSnapShot’s solutions are being used across 92 countries, where amateur photographers and professionals alike laud the user-friendly solution built on AWS. Perhaps the truest testimony of the power of the technology is that the popular global endurance event company Tough Mudder recently started using GeoSnapShot. Tough Mudder participants are often barely recognizable due to the unavoidable head-toe coating of mud inherent to the competition, and yet GeoSnapShot’s participant identification is successful. (No, competitors don’t need to upload a mud-covered selfie for it to work either; a more glamorous image works fine too!)

Tough Mudder’s VP of Live Events, Johnny Little, comments, “Reliving the memories made is vital to our participants and GeoSnapShot have an outstanding global photography platform that provides the best solution for every Tough Mudder event worldwide.”

Andy lauds AWS AI as the underpinning of that solution. “AWS provided us with the most flexible technology platform as we started building our business. As GeoSnapShot is a platform, it’s important we use leading technology to deliver the very best experience for all our customers. AWS continues to provide us with a world-leading technology. We are delighted with the access we have to the technology and business teams to drive future solutions.”

GeoSnapShot has chosen AWS as their primary AI/ML platform, and the company’s tech stack will get even deeper in the coming months, as the company is currently in the process of implementing a video solution in addition to the still photos. GeoSnapShot believes that providing athletes with memories of their achievements in stills and video, all using recognition technology, is the future of sports media.

Ultimately, GeoSnapShot wants every event and every photographer globally to have the opportunity to use its platform. Andy comments, “After all, memories of our lives and those of our loved ones are very precious and should be captured.”

To learn more about Amazon Rekognition, visit https://aws.amazon.com/rekognition/.

About the Author

Marisa Messina is on the AWS AI marketing team, where her job includes identifying the most innovative AWS-using customers and showcasing their inspiring stories. Prior to AWS, she worked on consumer-facing hardware and then university-facing cloud offerings at Microsoft. Outside of work, she enjoys exploring the Pacific Northwest hiking trails, cooking without recipes, and dancing in the rain.

 

 

 

[P] Testing The Limits of Grover The Neural Fake News Detector. Can it write fiction? Can it write riddles?

Written on . Posted in Reddit MachineLearning.

Grover is presently the top post or /r/machinelearning.

Grover is modeled after the full size 1.5 Billion Parameter GPT-2 model, and since they helpfully provided a web interface to it, this is a bit like getting a backdoor into the full size GPT-2 model that the public does not have access to. Update: They just switched to the 345M parameter model to conserve resources, I may be partly responsible for that…

I’ve been poking at Grover way too much over the last few days and I wrote up a few of my experiments. For example Grover can be kind of be used as a one-sentence summarizer or to guess the date of news. Grover can also write straight up fiction which is kind of weird since it’s training corpus was restricted to the 5000 Google News domains!

All the poking in this post was done on Thursday and Friday morning with the full size 1.5 B model, before they switched over to the smaller one:

https://iforcedabot.com/what-can-a-fake-news-detector-do/

(I picked [P] as the Tag, though maybe [D] is more appropriate?)

submitted by /u/JonathanFly
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[R] (ICML’19 AutoML workshop) InstaNAS: Instance-aware Neural Architecture Search

Written on . Posted in Reddit MachineLearning.

[R] (ICML’19 AutoML workshop) InstaNAS: Instance-aware Neural Architecture Search

https://i.redd.it/vuvexwob9k131.png

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TL;DL The first paper integrates Neural Architecture Search (NAS) with instance awareness and searches for a distribution of neural architectures to further expoit the power of NAS. InstaNAS achieves significant improvement in accuracy-latency tradeoff than single model. Our qualitative results show the controller policy shares many interesting similarities to human perception.

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Also see our other recent works:

  • COCO-GAN: Generation by Parts via Conditional Coordinating:TL;DR We show that it is possible to generate images by parts with a conditional coordinate mechanism. Our model preserves the state-of-the-art FID score and provides multiple interesting applications.Project Page: https://hubert0527.github.io/COCO-GAN/
  • Point-to-point Video Generation:TL;DR We propose a new paradigm for controllable video generation between a pair of start- and end-frame. We show that our approach can generate various lengths of videos without loss of quality and diversity. We also showcase several new extensions with the proposed framework.Project Page: https://zswang666.github.io/P2PVG-Project-Page/
  • Conditional Cost-Volume Normalization for Sparse Sensory Data:TL;DR We investigate and propose a novel normalization module for fusing sparse sensory data (3D LiDAR) and dense imagery data (stereo image). We show that our method effectively utilizes sparse sensory and brings significant performance, robustness and sensitivity improvement.Project Page: https://zswang666.github.io/Stereo-LiDAR-CCVNorm-Project-Page/

submitted by /u/hubert0527
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[D] Deploying deep learning models into production without HTTP overhead

Written on . Posted in Reddit MachineLearning.

Hi folks,

when it comes to deployment and integration of deep learning models, it seems like there are a lot of solutions out there, but the most are kind of complex or depend on third party solutions. While it often seems to be a good idea to containerize models and deploy them the modern microservices way, I think this approach is kind of heavy and needs a lot of infrastructure, therefore is not the best idea as small or medium sized company.

I want to share an alternative approach, which can be used easily by just utilizing what python and e.g. the Java ecosystem offers out-of-the-box. Link

What about you, how do you productionize deep neural networks?

Cheers

submitted by /u/ixeption
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[D] How to approach “Price prediction” Machine Learning problem on time series data?

Written on . Posted in Reddit MachineLearning.

My initial thoughts

I got a time series transaction data of purchase logs of different products.

Ex: Product named “Y” purchased for “M” amount, on Date “D” at Location/Place “P” and 5-10 other features.

Now i need to predict, how much we need to pay for purchasing Product named “Y” on new Date “D1” at Location/Place “P1” and same 5-10 other features.

Need inputs on choosing

  1. Machine Learning library – I have TensorFlow 2.0 in my mind.

  2. ML Model / Neural network arch need to choose – I have Linear Regression in my mind.

  3. Training strategy – using TF 2.0

  4. Inference/Prediction strategy – using TF 2.0

  5. Model saving, loading, versioning. – Saving to / Loading from / versioning using AWS S3.

  6. Machine Configuration – Dataset is small so i am Okay with Running on CPU.

submitted by /u/pktippa
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