YOLO v3 complete architecture


I am attempting to implement YOLO v3 in Tensorflow-Keras from scratch, with the aim of training my own model on a custom dataset. By that, I mean without using pretrained weights. I have gone through all three papers for YOLOv1, YOLOv2(YOLO9000) and YOLOv3, and find that although Darknet53 is used as a feature extractor for YOLOv3, I am unable to point out the complete architecture which extends after that - the "detection" layers talked about here. After a lot of reading on blog posts from Medium, kdnuggets and other similar sites, I ended up with a few significant questions:

  • Have I have missed the complete architecture of the detection layers (that extend after Darknet53 used for feature extraction) in YOLOv3 paper somewhere?
  • The author seems to use different image sizes at different stages of training. Does the network automatically do this upscaling/downscaling of images?
  • For preprocessing the images, is it really just enough to resize them and then normalize it (dividing by 255)?

Please be kind enough to point me in the right direction. I appreciate the help!


Posted 2019-03-28T03:07:34.627

Reputation: 201

No answers