From the course: Deep Learning with Python: Convolutional Neural Networks

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Using a pretrained model for object detection in Python

Using a pretrained model for object detection in Python - Python Tutorial

From the course: Deep Learning with Python: Convolutional Neural Networks

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Using a pretrained model for object detection in Python

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- [Instructor] In this tutorial, we're going to use a pre-trained YOLO model for object detection. Before we begin, let's select our Kernel. By the end of this tutorial, you'll know how to load and prepare a YOLO object detection model for inference. You also know how to run inference and inspect raw detection outputs. Finally, you'll know how to draw and display bounding boxes with class labels and confidence scores. The first thing we do is import the YOLO class from the "ultra lytics package". This allows us to load a pre-trained object detection model. So let's go ahead and run that. So the next thing we now do is specify the path to the image that we want to process. So let's go ahead and create a variable called image path that has the path to the image we want to process. So before we actually process the image, let's preview it. This is something we do just to verify that the path and the image that we've specified is the one that we intend to process. Let's run this here to…

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