A Gentle Introduction to Object Recognition With Deep Learning
Last Updated on July 5, 2019
It can be challenging for beginners to distinguish between different related computer vision tasks.
For example, image classification is straight forward, but the differences between object localization and object detection can be confusing, especially when all three tasks may be just as equally referred to as object recognition.
Image classification involves assigning a class label to an image, whereas object localization involves drawing a bounding box around one or more objects in an image. Object detection is more challenging and combines these two tasks and draws a bounding box around each object of interest in the image and assigns them a class label. Together, all of these problems are referred to as object recognition.
In this post, you will discover a gentle introduction to the problem of object recognition and state-of-the-art deep learning models designed to address it.
After reading this post, you will know:
- Object recognition is refers to a collection of related tasks for identifying objects in digital photographs.
- Region-Based Convolutional Neural Networks, or R-CNNs, are a family of techniques for addressing object localization and recognition tasks, designed for model performance.
- You Only Look Once, or YOLO, is a second
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