Algorithmia

Introduction to Video Tag Sequencing

Video Metadata Extraction

Sifting through unlabelled videos can be difficult and time-consuming. Even for the most seasoned analyst, fatigue leads to mistakes. Whether you’re trying to detect anomalies in mission-critical infrastructure — or you just want to find all of the segments in your vacation videos that contain ducks — we have a microservice that can help reduce the workload.

What is the Video Tag Sequencer? How does it work?

The VideoTagSequencer is an algorithm takes the time series point data generated from VideoMetadataExtraction and converts it into an index of detected labels and sequences contained in the video. In a nutshell, it takes frame-by-frame results, and converts them into a list of time ranges at which each result occurs. Read More…

One-page Web Tool: Transfer Style from One Image to Another in JavaScript alone

You may have already seen how to do style transfer via the StyleThief microservice in Python, but let’s take a different approach: what if I wanted to create a one-page website out of the service, without having to create any backend? That’s right: using only JavaScript and HTML, we’ll make it possible for website visitors to make their own images look like a Van Gogh, a Picasso, or any other piece of art they can find a digital photo of. Read More…

Style Transfer with StyleThief

Style transfer is a term used for reimagining an image with the style of a given piece of art. Recently, various research groups have proposed different approaches to do style transfer. Generally speaking, there are trade-offs between these different techniques.

For example, in one of our previous spotlights we talked about DeepFilter, where you train a model based on a style, and stylize images almost instantaneously with that trained model. You would train for about a day, and later be able to stylize images rapidly. The biggest issue with this technique is that training wouldn’t always yield the best results. You would then need to train it multiple times, which could easily add up to a few days.

StyleThief works differently from DeepFilter. It takes a long time to train for every sample image, but is more robust and yields better stylized images. It is a trade-off between speed and quality. Read More…

Train a Face Recognition Model to Recognize Celebrities

Sam Trammell and Rustina Wesley

Sam Trammell and Rustina Wesley from True Blood

Earlier this week we introduced Face Recognition, a trainable model that is hosted on Algorithmia. This model enables you to train images of people that you want the model to recognize and then you can pass in unseen images to the model to get a prediction score.

The great thing about this algorithm is that you don’t have to have a huge dataset to get a high accuracy on the prediction scores of unseen images. The Face Recognition algorithm trains your data quickly using at least ten images of each person that you wish to train on. Read More…

Quickly Building a Face Recognizer

Have you ever wondered how companies like Facebook automatically tag millions of user images?

Or did you find yourself in a situation where you want to automate tagging people in images… perhaps with tens of thousands of images?

Or maybe you just want to build a simple TV show celebrity classifier for your fan site?

Well now there’s a quick and efficient way of doing this today. And it scales seamlessly! Read More…