With nearly 1.5 billion monthly visitors and 346,000 pictures of tattoos, Tattoodo is taking advantage of deep learning to help categorize the growing number of uploaded images.
“At Tattoodo, we spend a lot of time and effort on classifying the tattoo pictures that are uploaded,” mentioned Goran Vuksic, a developer at Tattoodo. “A community member is able to provide a textual description and tag the tattoo with arbitrary hashtags, which obviously is a lot of responsibility to put in the hands of one member.”
To help tackle this problem, Vuksic and a few others came up with a deep learning project during the company’s “Hacker Day” in hopes to classify the remainder of the images with missing descriptions and also to suggest hashtags during the upload and edit process.
Using DIGITS, GPUs on the Amazon cloud and the cuDNN-accelerated Caffe deep learning framework, the developers trained their neural networks on 13 unique styles of tattoos and their related common hashtags.
The internal hacker project is still in beta as they continue to improve results, but plan on rolling it into production once they achieve the highest possible accuracy.
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AI-Generated Summary
- Tattoodo, a platform with nearly 1.5 billion monthly visitors, is using deep learning to categorize the large number of tattoo images uploaded to their site.
- Goran Vuksic and his team developed a deep learning project using DIGITS, NVIDIA GPUs on Amazon cloud, and the cuDNN-accelerated Caffe framework to classify tattoo images and suggest hashtags.
- The project, still in beta, was trained on 13 unique tattoo styles and their related hashtags, and aims to improve the accuracy of image classification and hashtag suggestions.
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