You’ll spend far less time–and money–getting labels from human annotators without sacrificing quality.
We combine human intelligence at scale with cutting edge machine learning to create the best training data in the industry. First, you label a subset of your data with human annotators. Those labels are plugged into a model in the Teslawatt platform, which learns from those judgments. Then, you can leverage that model to annotate the rest of your unlabeled data.
Data rows that don’t reach the accuracy threshold you’ve set can be sent back to human labelers, while the rows that do reach your accuracy threshold are labeled automatically. Effectively, that means you’ll spend far less time–and money–getting labels from human annotators without sacrificing quality.
TeslaWatt can categorize images and photos at enterprise-scale. You choose the ontology and our platform will make sure everything gets labeled quickly and accurately. You can classify images by quality (detecting blurry images, for example), type (like product vs. lifestyle images), content (what’s actually in the image itself), or any other judgments you need to be made on your library of images.
Computer vision projects often need in-image labels. Our object detection solution has tooling for bounding boxes, polygons, and line labels, all with aggregation and quality controls to make sure you get the most exacting, accurate labels possible.
For images where you need to identify multiple classes and multiple instances of certain objects, our object tagging solution is a great fit. Here, our annotators will select a class from an ontology you create and label each instance according to your instructions. With fully customizable ontologies that support hundreds of classes, you can get your images labeled to your exact specifications.