Speeds Up Identifying Which Images Need Location Metadata With Shotzr

How Shotzr used the Appen platform to improve the recommendation and search experience for their customers

For us, a big part of the challenge of making machine learning practical has been shrinking the cycle time between idea and production. We knew we were making progress when we could rapidly rebalance our training data and integrate feedback from our ML team into the process. The iterative cycle sped up dramatically, and we could move our models from idea to production in record time. – Mark Lemmons, co-founder of Shotzr
 

The Company

In a world with nearly 10 billion screens, customer experience and attention are driven through imagery. Shotzr helps you identify your customer, and then identify with them through contextual imagery that matches your audience targeting. Whether you’re doing Facebook and Instagram campaigns, social media marketing, SEM landing pages, or truly dynamic creative, Shotzr provides you with all the imagery you need for digital marketing.  

The Challenge

Shotzr’s collection of curated, high-quality imagery is growing at a breakneck pace with access to nearly 100M images. In order to deliver the exact image marketers need for every marketing moment, Shotzr’s images need to be properly and accurately annotated. As Shotzr’s contributors upload more images, and Shotzr’s partnerships with other image providers continue to grow, the in-house team could not keep up with labeling and metadata demands, making it difficult to scale. Shotzr annotated approximately 20,000 images over 90 days, but that wasn’t going to scale to the massive amounts of images on the platform. To ensure Shotzr’s platform serves up the right images that marketers are looking for, Shotzr needed to create a solution identifying images that needed specific labels. These labels display only the shortlist of perfect options to customers, as opposed to scrolling through hundreds of images that match the search query. For example, some images are clearly from a specific location – like Aspen, Colorado. Labeling this location can help people trying to find specific photos of Aspen, not the trees, but the town and surrounding areas. But some images don’t have a distinguishable location – like a photograph of a bunch of hands or a typical stock photo.  

The Solution

By turning to machine learning, Shotzr wanted to speed up and automate the process of identifying which images needed to have specific location metadata. Proving value early on and actually delivering that value with ML was critical to Shotzr’s success with moving from pilot to production because of their initial limited resources for making the model work. To prove that value, Shotzr was relying on obtaining high-quality data to properly and accurately train their models. To offer that level of granular labeling, Shotzr wanted a data partner that was up to the challenge of the volume of work, for a company their size, and with rational pricing. This is why they turned to us for help with the data that fed into their search relevance model.  

The Result

Fast forward to only a few weeks later, and Shotzr is anticipating training 4x as many classifiers as they thought possible. After their first job in the Appen platform, Shotzr identified over 17,000 images that did not require additional labeling. They anticipate over 61 million assets that they can remove from consideration for location data, freeing up their time to focus on images that can benefit from location data, as well as creating new models to automate the location labeling process.

More Case Studies Like This

All Case Studies

Case Studies

CLEAR Global and Sheng Language

The Company: CLEAR Global, previously Translators Without Borders, is a nonprofit organization helping people get vital information and be heard, whatever language they speak. The Project: Continuing the pro-bono partnership with Appen, CLEAR Global is keen to develop chatbots for mental health, like a voice hotline, in regions with limited literacy. The first language to tackle was Sheng, a Swahili-English …  

Case Studies

Phone with social media apps

Creating a Safer Web Experience

The Project: Creating a Safer Web Experience for All  Trust Lab, a trusted company founded by senior Trust & Safety executives from Google, YouTube, Reddit and TikTok, is ensuring the internet remains a safe place for all. They are doing this through sentiment analysis to see what real internet users think about various content that is shared on the world …  

Case Studies

Combating Wildfires with Computer Vision

The Project: Training Machines to Combat Wildfires  A leading global security and aerospace company is enhancing its capability to combat wildfires through computer vision applications. These machine learning applications are used by decision support systems to track the predictive path of a wildfire. According to the National Interagency Fire Center, year-to-date acres burned for the US is approximately 116% of …  

Get in touch with Sales

Join our team!

Join our Crowd

Have a question? We’d love to help.

ImageImage
Join Our Crowd
@ 2022 Appen Limited
Legal Policies
Data Subject Access Request
@ 2022 Appen Limited
Legal Policies
Data Subject Access Request
Website for deploying AI with world class training data
Language
Chinese
Japanese