Michael Abramov

Computer vision is one of the hottest subfields of artificial intelligence at the moment. But none of the revolutionary applications we’ve heard about — from self-driving cars to autonomous drones — would be possible without image annotation. But image annotation for deep learning isn’t as straightforward as you might think.

Computer vision is revolutionizing industries. We’ve all heard of self-driving cars and facial recognition software. But how is this transformative technology making a difference in our day-to-day lives? Here’s a glimpse of how computer vision is transforming financial, retail, and medical sectors in unexpected ways: * Leave without paying.

Why do artificial intelligence companies spend so much time creating and refining training datasets for machine learning image processing? The answer is simple. All else being equal, the higher the quality of your training datasets, the better your machine learning application will perform. But what determines the high-level quality of

In the world of AI, data is king. And while data annotation may be the least glamorous aspect of machine learning, it’s also one of the most important. That’s because the performance of your algorithms hinges directly on the quality of the data you use to train them.

Bounding boxes are one of the most popular—and recognized tools when it comes to image processing for image and video annotation projects. Image processing techniques is one of the main reasons why computer vision continues to improve and drive innovative AI-based technologies. From self-driving cars to facial recognition technology—

Accurately labeled datasets are the raw material for the machine learning algorithms and deep learning revolution. Vast quantities of data are required to train new target object generations of Artificial Intelligence (AI). Correctly labeled images train computer vision systems to reliably distinguish between a stop sign and pedestrian. Or it