Thanks 🙂

Wonderful feedback, Gota. I'll make sure to create them in the future

Thanks, Jeffrey! Wait for the better ones then 😄

Thanks, Raghuveer! Appreciate it.

Glad it was helpful!

Glad you enjoyed it!

Glad you liked it!

Glad you liked it :)

Thanks, Muhammad. I actually want to create videos for other object detection algorithms as well and put them in a playlist. From my past experience and based on the videos I've seen, usually, long videos get more viewers. I already separated this video into different chapters and viewers can watch each one on their own time. It's a kinda subjective opinion I believe.

@@soroushmehraban how about Yolo?

Thank you, Bellz!

Thanks!

Thanks, Seokeon. I hope you find it useful.

Thanks!

Thanks, Alexander!

Glad you liked it!

Thanks Aref

Thanks, Kaan!

Thanks for the positive energy, Chayan!

Glad you liked it!

Thanks, Ishaan. Sure!

Thanks, Mohamed!

Thanks, Yaser. I'll post them. But first I'll post Fast R-CNN

Thanks, Can! Appreciate it.

Thanks, Alireza. I hope you found it useful.

Thanks, Pouya.

Thanks for the honest feedback 😃 looking at the previous videos posted, I’m not using that phrase anymore.

@@soroushmehraban Oh I spoke too fast, (bc I watched some parts of the video several times, I thought you used the expression several times)... Yeah I take it back apologies, oc everyone can use this expression!

Thanks, Lakshay.

Thanks for the feedback Huy 🙂It's just a powerpoint.

Thanks!

Thanks, Mohandes. I'll try enhancing the quality by changing my recording method but still it's not gonna be perfect. At least not in the first few videos.

I don't remember that was a long time ago. I'm not using any background music anymore.

Thanks, Louis.

That's a great question. I think I should have mentioned that. Our model might predict different bounding boxes pointing to the same object. In such a scenario, we do the following: 1) Sort all the predicted bounding boxes based on the class score (In descending order). 2) Pick the first bounding box that has the highest probability score. 3) Compute the IoU of the selected bounding box with other bounding boxes pointing to the same class. 4) If the IoU of any bounding box with this bounding box is larger than a threshold (such as 0.5), then we remove the bounding box having the lower class score. I hope it's clear.

​@@soroushmehraban i think following conditions might not be sufficient, because even if we sort and pick highest one... again we left with question of all these are pointing to same object location or reference really in a image ? same object references might be at multiple places please clarify this doubt

That's true we might have same objects at multiple places. let's say we have object A at location (x1, y1) and (x2, y2). for location (x1, y1) our model might predict multiple bounding boxes all refer to the object A. Out of all these bounding boxes we only keep the one that has the highest score and others if they have IOU higher than a threshold with this bounding box, we remove them. For object A at place (x2, y2), since it's in different area of the image, the IoU with the one having highest score is less than a threshold, so we keep the second one having the highest threshold and again others having IoU higher than a threshold, we remove them. @@NagarajuSeru-rc7lb

Thanks, man! I'll try my best.