![]() In this article, we will explore object detection using YOLOv8. Now you can use a single platform for all these problems. Fortunately, things changed after the YOLO created. There are many different neural network architectures developed for these tasks, and for each of them you had to use a separate network in the past. Object detection neural networks can also detect several objects in the image and their bounding boxes.įinally, in addition to object types and bounding boxes, the neural network trained for image segmentation detects the shapes of the objects, as shown on the right image. The neural network for object detection, in addition to the object type and probability, returns the coordinates of the object on the image: x, y, width and height, as shown on the second image. The neural network that's created and trained for image classification determines a class of object on the image and returns its name and the probability of this prediction.įor example, on the left image, it returned that this is a "cat" and that the confidence level of this prediction is 92% (0.92). All these methods detect objects in images or in videos in different ways, as you can see in the image below: Common computer vision problems - classification, detection, and segmentation You can use the YOLOv8 network to solve classification, object detection, and image segmentation problems.
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