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The data annotation process is the engine of any computer vision based AI project. In order to create cutting edge machine learning models with world changing applications, innovators need access to precise training data at scale. In order to provide this level of labeled image and video data companies and

Powerful, observant and consistent security systems are essential for keeping public areas and businesses safe. Detecting violent threats, dangerous objects and potential theft early helps to protect these spaces. This has traditionally been the job of security staff, who monitor security camera footage. However, security teams are now being supported

Computer vision AI systems have the capacity to reliably identify objects in digital images. AI developers are using this capability in a range of industries to improve efficiency, customer experience and safety. Object recognition may be of particular value in the retail sector. Retailers are beginning to introduce AI into

Online shopping is more popular than ever. Increasingly consumers are choosing to do the majority of their fashion shopping through websites and apps. Retailers have responded by making the online shopping process easier. To do this companies are turning to computer vision AI to create online fitting experiences where customers

Computer vision AI is helping farmers to improve yields and strengthen the global food supply. Advances in machine learning have led to a range of applications that help growers to monitor and protect their crops. However, these important use cases are made possible by data annotation, the process that creates

Finding and preserving archaeological history is a vital task. Historical sites must be found so that they can be protected, valuable archaeological items must be identified so that we can learn more about the past, and the human remains of our ancestors must be safeguarded. The internet has led to

Production defects can lead to dissatisfied customers at best and dangerous accidents at worst. It is in the interests of producers in all industries to reduce the amount, and severity of production errors. This task can be a difficult one however, with rigorous quality control procedures requiring extensive investment in