'How Much Data is Enough for Small Dataset-Based Object Detection?' machinelearning objectdetection
The goal of object detection is to find objects with certain characteristics in a digital image or video with the help of machine learning. Object detection is a branch of computer vision that deals with identifying and locating objects in a photo or video. Machine learning is used in self-driving cars, pedestrian detection, and optimizing traffic flow in cities.
Getting started with any machine learning project often starts with the question: “How much data is enough?”. The response depends on several factors like the diversity of production data, the availability of open-source datasets, the expected performance of the system, and the list can go on for quite a while. In this article, I’d like to debunk a popular myth about machines only learning from large amounts of data, and share a use case of applying ML with a small dataset.
With the rapid adoption of deep learning in computer vision, there are more and more diverse tasks needed to be solved with the help of machines. To understand machine learning applications in the real world, let’s focus on the task of object detection.Object detection is a branch of computer vision that deals with identifying and locating objects in a photo or video.
. Machine learning algorithms allow to quickly detect any defects, automatically count and locate objects. This allows them to improve inventory accuracy by minimizing human error and the time spent.Machine learning is used in self-driving cars, pedestrian detection, and optimizing traffic flow in cities. Object detection is used to perceive vehicles and obstacles surrounding the driver. In transportation, object recognition is used to detect and count vehicles.
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