Torch implementation of SegNet and deconvolutional network

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Deep Learning segnet-torch
Overview
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Comments
  • Input/target dimensions with cudnn.SpatialCrossEntropyCriterion

    Input/target dimensions with cudnn.SpatialCrossEntropyCriterion

    Hi, thank you for your impressive work! Could you please explain the required dimensionality of outputs and targets to use it with cudnn.SpatialCrossEntropyCriterion() as in your Yandex repo? Segnet model input is [batchSize, numChannels, H, W]and the output is [batchSize, numClasses, H, W]. But cudnn.SpatialCrossEntropyCriterion() requires the target to be of size [batchSize, H, W]. So it looks like that the model outputs numClasses separate masks for each class while the criterion expects a single mask with all the classes within it. I can't get through this issue while reproducing the model with a toy example.

    opened by rracinskij 2
Owner
Fedor Chervinskii
Fedor Chervinskii
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