knows that object detection networks are more complex, more involved, and take multiple orders of magnitude and more effort to implement compared to traditional image classification. Hey Adrian, if I have a Convolutional Neural Network trained for image classification, how in the world am I going to use it for object detection?īased on your explanation above, it seems like image classification and object detection are fundamentally different, requiring two different types of network architectures.Īnd essentially, that is correct - object detection does require a specialized network architecture.Īnyone who has read papers on Faster R-CNN, Single Shot Detectors (SSDs), YOLO, RetinaNet, etc. How can we turn any deep learning image classifier into an object detector? Today, you’ll see an example of this pattern in action. The probability/confidence score associated with each bounding box and class label The class label associated with each of the bounding boxes A list of bounding boxes, or the (x, y)-coordinates for each object in an image Input: An image that we wish to apply object detection to
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