Hi, There is something that I do’nt understand about the implementation of the yolo_non_max_suppression() function in course 4, week 3’s first programming assignment. This function takes as input the output from yolo_filter_boxes(), i.e., three tensors that represent all boxes that remain after th…

YOLO has different versions with different implementations. V3 Keras version has a same function, yolo_eval(), as this exercise, but it calls tf.image.non_max_suppression() by class. So, tf.image.non_max_suppression() is not class-aware, but yolo_eval() covers. And, recent implementations use mu…

@balaji.ambresh I had not found this one in my search, thank you for pointing me to it! @anon57530071 Aha… Let me see if I get this right. So you are saying that In our (example) programming exercise, which seems to be a bit simplified, we do indeed not carry out NMS independently for the classes…

First of all, we are not discussing about the real-world implementation at all. That’s important point. In the case of a real-world implementation, there are lots of stakeholders. The architecture decision points are sometimes different based on objectives, business needs, time-to-market, resourc…

@anon57530071 Yes, I think I got it now. Now it’s actually pretty obvious :slight_smile: It would have been nice if this had been mentioned in the programming exercise (since the lectures mention the need for a per-class handling), then I wouldn’t have confused myself so much! Thanks again!

Course 4, week 3, programming assignment 1: non-max suppression and multiple classes

Course Q&A Deep Learning Specialization Convolutional Neural Networks

balaji.ambresh August 2, 2022, 12:34pm 2

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Course 4, week 3, programming assignment 1: non-max suppression and multiple classes

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