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Busting common misbeliefs about data labeling workflows and budgets to provide a realistic decision framework that will take your data labeling strategy from flawed to flawless.

Although 2D camera data is used to teach autonomous vehicles to find their way from Point A to PointB, it comes with its own set of drawbacks. For eg: camera images are not very useful when it is dark or there are reflections due to strong sunlight

Machine learning algorithms are designed so that they can “learn” from their mistakes and “update” themselves using the training data we provide them. But how do they quantify these mistakes?

Machines are much faster at processing and storing knowledge compared to humans. But how can one leverage their speed to create intelligent machines? The answer to this question – make them feed on relevant data. This is also referred to as Training data.

The most important thing that the computer vision expert does is to decide which image annotation type is needed to build the most accurate model(s) for object detection.

I have briefly written about the ways you can start gathering training data. This depends majorly on the use case you plan to work on.

Sunday Afternoon. You come across a magazine ad for solitaires, but it is the model wearing a perfect white sundress that catches your attention. What if you could take a picture of the dress and upload it, so an app could help you find a dress just like that one?