I stumbled upon this thread while searching for inspiration on the web, as I’m also trying to implement yolov3 from scratch on pytorch. I have to say, the implementations I’ve found here are indeed much more understandable and straightforward then other ones I’ve found.
However, I can’t seem to wrap my head around why there are no route layers on the implementations listed here. Moreover, the original yolov3 paper doesn’t mention the existence of such layers (see table 1 on the paper), even though they are mentioned in the cfg file on the authors github.
Can any kind soul provide me some clarification?
Thanks in advance!
Hey Antonio, the discussion here refers to just the convolutional base of YOLOv3, which is Darknet-53. The route layers are for constructing the U-net/feature pyramid network that sits on top of the Darknet-53 base. Hope that helps.
I’m working on a full implementation of YOLOv3 in fastai v1. Has anyone managed to get the pre-trained Darknet-53 weights into the fastai implementation of Darknet? I’d like to avoid training the network from scratch.
Ben, I would look at https://github.com/ayooshkathuria/pytorch-yolo-v3/blob/master/README.md
They read in the weights from darknet. There is also a good blog on paperspace that explains things to go with it. The other repo to look at is
It implements training with transfer learning from the darknet weight files.