Machine learning works by Building ‘smart algorithms’ and present the computer with ‘enough’ real-world examples of the environment (training data), so that when the computer sees ‘similar data’, it knows what to do.
In order to stay at the top, machine learning models need to be trained on representative datasets that include all the needed all possible circumstances and possibilities
Some examples:
- Traffic cameras that automatically detect lane violations.
- Fitness applications that automatically log your calorie count from pictures of the food you eat. You don’t have to input the amount and type of food anymore.
- Security cameras that annotate the root cause of motion sensor triggers (e.g. whether it was an animal, human, falling leaves, a car driving by, etc.) and react accordingly. It also helps decrease the frequency of false alarms.
For these Computer Vision models to work in real world with best accuracy, curated (labeled) data sets are used by ML experts to train algorithms by adjusting parameters, in order to make accurate predictions for incoming data.
