Tutorial on implementing YOLO v3 from scratch in PyTorch



Object detection is a domain that has benefitted immensely from the recent developments in deep learning. Recent years have seen people develop many algorithms for object detection, some of which include YOLO, SSD, Mask RCNN and RetinaNet.

This is a companion discussion topic for the original entry at https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch


Cool video from Karol Majek showing his results in YOLO v3:


Hi, it seems it does not work when I choose a different batch size in the detect.py? Anybody have solutions or ideas about this? Thank you very much!


@DongDong_Chen Hmm interesting. The author is working on a full tutorial on the training component itself. Should be out soon.


@DongDong_Chen It seems as if you’ve cloned my other pytorch v3 repo, and not the one linked in this tutorial. There was a bug that made the code crash with a batch size of > 1, and that has been resolved. The issue doesn’t exist in repo linked in this post.


Is there a section how to run a training job for YOLO on Paperspace?


thanks for the details of YOLOV3. I get the great overview of the whole algorithm goes with you r instruction. but I still have a small puzzle here.
on the making predictions part, the computation about “bw and bh” I still unclear. there you said" tw, th is what the network outputs. pw and ph are anchors dimensions for the box.". About the pw and ph, does it mean pw and ph is the real length of the width and heigh of anchors box in the original image(416*416), or sth else?


I have the same puzzle ,are you understand it?


Hello, @ayooshkathuria great tutorial. i need some suggestion, is it possible to detect only one object? like if I want to detect a car will it detect car only and ignore rest of the object in the image or video.