Where’s the better way using pandas? Anyone have the link handy?
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Going back to drop out rate:
Shall we play around with it? Is there any utility class/fct in pytorch that automatically adjusts the weights when dropout rate of a layer is changed?
Or accommodation of adding/deleting a drop out layer and weight adjustment?
What was the loss function used for multi-class classification?
in my experience, drop out rate is just something you have to try different values for until you get something that works well for you
It’s here.
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you can apply some kind of hyperparameter optimization algorithm to it if you want to be systematic
Can Jeremy explain use_clr=(32,5) usage in learn.fit?
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any reason in the bbox only, you did not use sigmoid * 224 to bound the output of bbox prediction but you use it in the bbox and cat prediction loss function?
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When designing a loss function with inputs of multiple kinds (i.e. L1 loss for bounding boxes and maximum likelihood loss for classes), how do we control weights for these kinds, like Jeremy did it in the lecture by multiplying one of them by 20, but without manual examination?
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Well, you’ll have to throw away your old model unless you adjust the weights.
So, do you just use a new model with new dropout rates every time and see which one has the best loss?
is this concept the method behind most fully-convolutional nets? i’m thinking back to this paper - https://vision.cornell.edu/se3/wp-content/uploads/2017/07/LCDet_CVPRW.pdf
How do we know how many objects will we have in an image?
The idea is to essentially bring both the losses to the same scale range. That’s still manual. Maybe there’s a better way of doing it.
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with stride 2 how would be come up with channels to be 4+c
Does it matter how many objects are in the image?
Are we just mapping the remaining outputs to all 0 when there are fewer than 16 objects?
Again, please forgive my ignorance. What does YOLO stand for in this case?
How to choose anchor boxes ? Does it matter if a anchor box has only a part of an object and another box has rest of the object ?
You Only Look Once
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