Advance inspection experts is dedicated to helping manufacturers improve their vision inspection process. The AI-powered visual quality inspection goes beyond the capabilities of traditional machine vision in detecting anomalies and defects, even when products have natural variations.
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Datasets are the core of every deep learning project. They often contain tens of thousands of images spread through tens of teams for annotation and quality control. Dataset management helps you with integrations with other services as well as performing time consuming tasks in the matter of seconds.
As it is much said, data preparation is one of the greatest challenges any team willing to delve into the Deep Learning realm has to face. It’s no secret that challenges become more doable and easy when done with a companion and that’s why we’ve not only incorporated team management for dataset tasks into the core of our system, but we help you with our AI driven anotation companion.
FInding quality data in large numbers often prove to be very challenging for deep learning team. At these times augmentation techniques come into play. Essentially building new data from the data you have already gathered to enlarge the data and halp you with data gathering
Quality of dataset plays as much, if not bigger, role in model training result as the size of dataset. Unlike the size of dataset, which is quantative, analyzing the quality of dataset and interpreting the data regarding it is much more difficult and without the proper tools, user is essentially walking in the dark. We have come up with some of the most extensive healthcheck tools to help you build better datasets.
Everything done in deep learning teams, from te data gathering and data prepataion to augmentation and such are done for the purpose of training a model. Model training requires both a relatively deep knowledge of deep learning and deep knowledge of infrastructure and devops. With one of the most complete model libraries in industry, training a model has nver been this easy.
Finding quality data in large numbers often prove to be very challenging for deep learning team. At these times augmentation techniques come into play. Essentially building new data from the data you have already gathered to enlarge the data and halp you with data gathering
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