deep-learning


Should I scale the ground truth images in semantic segmentation?


I am applying CNNs for semantic segmentation. I am hoping someone here can recommend. What I am doing now is that I scaled ground truth images. I have 5 classes that I scaled them to (0-1) in Data Layer based on this:
transform_param {
scale: 0.00390625
}
I am wondering whether I am wrong or right? Is this scaling value correct?
whenever I am not adding `
it is showing the following error:
[...]
I0510 23:21:28.086776 9072 solver.cpp:397] Test net output #0: accuracy = 0
I0510 23:21:28.086812 9072 solver.cpp:397] Test net output #1: loss = 1.9416 (* 1 = 1.9416 loss)
F0510 23:21:28.150539 9072 math_functions.cu:141] Check failed: status == CUBLAS_STATUS_SUCCESS (11 vs. 0) CUBLAS_STATUS_MAPPING_ERROR
*** Check failure stack trace: ***
# 0x7fb9d4e7f5cd google::LogMessage::Fail()
# 0x7fb9d4e81433 google::LogMessage::SendToLog()
# 0x7fb9d4e7f15b google::LogMessage::Flush()
# 0x7fb9d4e81e1e google::LogMessageFatal::~LogMessageFatal()
# 0x7fb9d56665ea caffe::caffe_gpu_asum<>()
# 0x7fb9d5633a38 caffe::SoftmaxWithLossLayer<>::Forward_gpu()
# 0x7fb9d54bde41 caffe::Net<>::ForwardFromTo()
# 0x7fb9d54bdf47 caffe::Net<>::Forward()
# 0x7fb9d54e8d28 caffe::Solver<>::Step()
# 0x7fb9d54e98ca caffe::Solver<>::Solve()
# 0x40acd4 train()
# 0x407418 main
# 0x7fb9d360f830 __libc_start_main
# 0x407ce9 _start
# (nil) (unknown)

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