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How to know if the faster R-CNN is trained to a good state according to Mini-Batch loss and accuracy trends

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jingxue chen
jingxue chen on 12 Oct 2020
Answered: Divya Gaddipati on 29 Dec 2020
Hello, I am trying to train a faster R-CNN detector using my dataset, but the result is much worse than yolo V2, which is different from some papers. I think there is something wrong when training faster R-CNN. So I wonder how to know if I should change hyperparameters and when to stop training according to Mini-Batch loss and accuracy trends?

Answers (1)

Divya Gaddipati
Divya Gaddipati on 29 Dec 2020
Hi,
You should refer to this example in trainFasterRCNNObjectDetector documentation page to understand the role of various inputs going into trainingOptions function. For one, if you are training your network using adam solver, consider using the other training options associated with it like GradientDecayFactor and SquaredGradientDecayFactor.
You could also try couple of other things:
You can also refer to the answer posted for a similar question to understand how faster R-CNN works:

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