Recently, we introduced you to Video Transform, a meta-algorithm which can take any of our image transformations — such as Colorization, Saliency, Style Transfer, or Resolution Enhancement — and apply it to a video instead!
…but if picture is worth a thousand words, a video is worth 24-30,000 words per second (sorry, bad videography humor there). So instead of just telling you about this cool feature, we’d like to show you, with a brand new Video Toolbox demonstration!
Once upon a time, site mappers were arcane scripts which could take hours or days to examine a single website. But, thanks to scalable & interoperable cloud algorithms, it now takes only minutes… and includes a multitude of handy features powered by machine learning: auto-tagging, summarization, page-ranking, and more!
- GetLinks recursively traverses a website of your choice, plotting them on a force-directed graph via D3
- PageRank examines the pages to create an ordered list akin to Google’s PageRank Algorithm
- Url2Text grabs the text from each page, allowing Summarizer to extract topic sentences while AutoTag generates keywords
Learn more in our Introduction to Microservices article.
You may already know that Algorithmia hosts scalable deep learning models. If you are a developer, you’ve seen how easy it is to run over 3,000 microservices through any of our supported languages and frameworks.
But sometimes it’s nice just to play with a simple demo.
The Deep Fashion microservice is a deep CNN, performing multi-category classification, which has been trained with humans in the loop to recognize dozens of different articles of clothing. It can be used standalone to locate specific items in an image set, or combined with a nearest-neighbors service such as KNN or Annoy to recommend similar items to online shoppers. And since the service provides bounding box coordinates for each item within the image, it could even used to censor or modify images themselves.
To see it in action, just head over to the Deep Fashion Demo, click (or upload) an image, and watch as state-of-the-art deep learning models scan the image to identify clothing and fashion items.