Is it possible to constrain the bbox coordinates in some way? Say don’t let it exceed the image dimensions. Is there any evidence of this improving performance?
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It’s a common practice to use the L1 error since L2 error heavily penalises larger errors.
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Are those custom metrics (detn_loss, and detn_accuracy) on the training set or the validation set? Is there a way to specify?
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I tried L2 and the boxes were pretty bad =X
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I must have missed the “custom head” memo. Is there a section of a notebook somebody can link me to? Or a TL;DR that can be thrown in here?
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learn = ConvLearner.pretrained(f_model, md, custom_head=head_reg4)
basically retain everything in CNN except for the last layers, which you swap in your custom Sequential layers.
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We’re actually just using one dataloader to load the images, the other one only gives us the Y.
So we’re basically re-building a network, but keeping the last few layers of a pre-trained network?
we’re throwing away the last few layers.
we’re keeping the convolution and filters.
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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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