LeviViana:

Indeed, it is quite odd. My guess is that it will require a deep debug. BTW, did you check that the train and validation data-sets have the same distribution ?


I also switched some training images with the validation set and trained it. It gave me more or less the same results.



 LeviViana:

Otherwise, I’d pick two consecutive checkpoints: a good and a bad. Then, I’d pick a sample were both predict good results and a sample where only the good predicts the good results. Then, I’d look at the last activations, and see the differences. It may take a lot of time, and maybe the solution wouldn’t be straightforward.


Thats a good idea. I have already plotted some results with high loss. Usually the prediction is biased towards the biggest class, which is the background. (I have just 3 classes: moving car, static car and background, see illustration from [DeepMotion 2])

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