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Suprised to see this here since YOLO has been out for a while now. Shameless plug, I wrote an article on how to use use transfer learning on your custom dataset with the pretrained weights [1]. One of the downside of YOLO is that it uses his own deep learning library darknet. I find that the Tensorflow port dark flow easier to use but it haven't seen a v3 port yet.

[1] https://www.powu3.com/ml/yolo/




There is a pytorch port from Ultralytics (https://github.com/ultralytics/yolov3). Nobody seems to have figured out how to achieve the training performance of darknet though, which is entirely uncommented C. The source is all there, but the loss function changed between v2 and v3, and its not documented in the paper. I think it's been fixed in that pytorch port now though. The only frustrating thing is that every commit in the repo is called update...

Alternatively... you can train in darknet and then run inference in another framework of choice.

Also shameless plug: I wrote an annotation tool which is designed to output darknet formatted labels: https://github.com/jveitchmichaelis/deeplabel


Yeah, I don't remember where I read it but it took them a couple weeks to train the model from scratch. I tried training my own weights by scratch it was practically impossible using a Tesla K80. But it's pleasantly surprising how good the transfer learning results are on a custom data set. You can get some "state of the art" results when you train for a couple hours. It's really impressive how he came up with YOLO and wrote his own deep learning library from scratch.

Thank you for the links! I'm going to check both out. I want to see if the PyTorch port works with the new deployment feature from 1.0.





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