Video surveillance, autonomous driving, drone services - you name it. Computer vision models are increasingly being adopted across these industries to help make life easier.
Get a free demomanot is a CV performance monitoring platform that enables you to detect discrepancies between your training data and the real-world domain your model operates in. Explore some of the real-life use cases that are increasing day by day.
Automakers across the world, and several start-ups, are racing to build vehicles that can drive themselves. The variety of scenarios that the vehicle needs to be able to handle are extremely broad. Differing weather conditions, street signs that change from city to city, and road conditions are just some of the many factors that make creating a truly generalizable model for autonomous vehicles so difficult. For a model that constantly interacts with changing environments, it is imperative that the model is monitored in the production environment to detect outliers and likely fail points.
Video surveillance technology, coupled with smart algorithms that can detect intruders and irregularities has seen rapid adoption over the last decade. There are several challenges for ensuring computer vision for video surveillance works effectively and reliably. The variety of weapons that an intruder may use can vary greatly, leading to the system being unable to correctly detect an intruder or threat. The sheer amount of data that is produced by video surveillance systems also makes it difficult and costly to refine and improve the computer vision model that is operating on it. By monitoring the performance of the computer vision model, manot can help detect outliers in the video data produced by the cameras to improve the performance of your model efficiently.
As the world increasingly moves towards making purchases online, the need for efficient and cost-effective delivery methods is rapidly growing. Drone delivery systems are the latest innovative tool tackling the gaps in the market. Drones are also being utilized in emergency situations and for agrotech. Much like in the case of autonomous vehicles, drones constantly encounter changing environments. This makes it difficult to build a dataset that can capture the entirety of scenarios that the drone will encounter. Instead of simply adding massive amounts of data to the training set, manot allows you to monitor the performance of your drone’s computer vision model in the production environment and detect the outliers that will improve the accuracy of your model the most.
Automakers across the world, and several start-ups, are racing to build vehicles that can drive themselves. The variety of scenarios that the vehicle needs to be able to handle are extremely broad. Differing weather conditions, street signs that change from city to city, and road conditions are just some of the many factors that make creating a truly generalizable model for autonomous vehicles so difficult. For a model that constantly interacts with changing environments, it is imperative that the model is monitored in the production environment to detect outliers and likely fail points.
Video surveillance technology, coupled with smart algorithms that can detect intruders and irregularities has seen rapid adoption over the last decade. There are several challenges for ensuring computer vision for video surveillance works effectively and reliably. The variety of weapons that an intruder may use can vary greatly, leading to the system being unable to correctly detect an intruder or threat. The sheer amount of data that is produced by video surveillance systems also makes it difficult and costly to refine and improve the computer vision model that is operating on it. By monitoring the performance of the computer vision model, manot can help detect outliers in the video data produced by the cameras to improve the performance of your model efficiently.
As the world increasingly moves towards making purchases online, the need for efficient and cost-effective delivery methods is rapidly growing. Drone delivery systems are the latest innovative tool tackling the gaps in the market. Drones are also being utilized in emergency situations and for agrotech. Much like in the case of autonomous vehicles, drones constantly encounter changing environments. This makes it difficult to build a dataset that can capture the entirety of scenarios that the drone will encounter. Instead of simply adding massive amounts of data to the training set, manot allows you to monitor the performance of your drone’s computer vision model in the production environment and detect the outliers that will improve the accuracy of your model the most.
manot's data proposal system data proposal system suggests samples of data to add to your training dataset to improve the accuracy of your model.
Monitor the performance of your computer vision models in the production environment to see how it is operating.
Identify out-of-distribution data samples as they happen and gain insights on why your model is failing.
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