Developing the capacity to annotate massive volumes of data while maintaining quality is a function of the model development lifecycle that enterprises often underestimate. It's resource intensive and requires specialized expertise. At the heart of any successful machine learning/artificial intelligence (ML/AI) initiative is a commitment to high-quality training data and a pathway to quality data that is proven and well-defined. Without this quality data pipeline, the initiative is doomed to fail. Computer vision or data science teams often turn to external partners to develop their data training pipeline, and these partnerships drive model performance.

Its features and API are subject to significant change as development progresses. The Perception package provides a toolkit for generating large-scale datasets for computer vision training and validation. It is focused on a handful of camera-based use cases for now and will ultimately expand to other forms of sensors and machine learning tasks. Quick Installation Instructions Get your local Perception workspace up and running quickly. No prior Unity experience required.

We live in a world besotted with images: selfies, Instagram photos, video clips running on social media platforms that make it easy to instantly post images captured by smartphones and other devices. The explosion of images is making keyword searches less effective, prompting companies that sell photos and music to come up with new approaches to sorting through the haystack for the relevant image. With that in mind, image and music licensor Shutterstock Inc. (NYSE: SSTK) has launched new search features based on machine learning techniques and, specifically, its proprietary "convolutional" neural network technology. Convolutional neural networks are a machine-learning construct usually comprised of multiple layers that is often followed by more fully connected layers as in a standard multilayer neural network. Convolution nets are made up of neurons with "learnable" weights and biases.