A gentle guide to deep learning object detection - PyImageSearch

A gentle guide to deep learning object detection Today's blog post is inspired by PyImageSearch reader Ezekiel, who emailed me last week and asked: Hey Adrian, with the followup tutorial for real-time deep learning object detection . I've been using your source code in my example projects but I'm having two issues: How do I filter/ignore classes that I am uninterested in? How can I add new classes to my object detector? I would really appreciate it if you could cover this in a blog post. In fact, if you go through the comments section of my two most recent posts on deep learning object detection (linked above), you'll find that one of the most common questions is typically (paraphrased): How do I modify your source code to include my own object classes? Since this appears to be such a common question, and ultimately a misunderstanding on how neural networks/deep learning object detectors actually work, I decided to revisit the topic of deep learning object detection in today's blog post. Specifically, in this post you will learn: The differences between image classification and object detection The components of a deep learning object detector including the differences between an object detection framework and the base model itself How to perform deep learning object detection with a pre-trained model How you can filter and ignore predicted classes from a deep learning model Common misconceptions and misunderstandings when adding or removing classes from a deep neural network To learn more about deep learning object detections, and perhaps even debunk a few misconceptions or misunderstandings you may have with deep learning-based object detection, just keep reading. A gentle guide to deep learning object detection Today's blog post is meant to be a gentle introduction to deep learning-based object detection. I've done my best to provide a review of the components of deep learning object detectors, including OpenCV Python source code to perform deep learning using a pre-trained object detector.