MIDS Capstone Project Fall 2022

AniML: No Code ML Vision for Specialists with no CV experience

Introducing AniML, the no code AI computer vision tool that allows anyone, even those without a technical backgrounds, to easily build and implement their own computer vision systems. Take control of a chaotic image folder by uploading it to the platform where it automatically filters down to images with objects of interest. Label these representative images and easily train your own custom model which you can use to further streamline your workflow. Once trained, AniML models generate a detailed analytics dashboard full of rich insights.

AniML was originally designed to help biologists automatically sort through hundereds of thousands of field camera images tracking animal populations. Automating these previosuly manual process, AniML frees up biologists' time so they can focus where it counts - applying their unique skills to solve pressing global challenges, such as climate change, disease outbreaks, and food insecurity.

So far we've worked with biologists and biopharmacutical professionals to dramatically reduce the time needed for their inspection and manual classification tasks.

This platform allows users to:

Sort and cluster images into similar groups 

Take control of a chaotic dataset by uploading it to the platform - AniML automatically sorts and finds images with objects of interest, which you will later label and use to train a supervised ML model.

Train custom ML vision models

Using the filtered images from above, add labels detailing the class and location of each object you’re trying to detect. Using the labeled dataset, train your own custom ML model that will automatically detect objects of interest for you.

Run inference and generate insights 

With your new model, automate away all your previously manual steps! Upload new images and AniML will automatically find images with objects of interest AND generate a comprehensive dashboards of metrics detailing what the model found.





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Last updated:

December 9, 2022