New at this.. stuck at building a classifier using the imagenet tutorial

[Newbie]

Hi guys.. 

I am kinda new to this community, and this framework.. 

I am trying to build a classifier by following the tutorial http://caffe.berkeleyvision.org/gathered/examples/imagenet.html

My idea is to building a classifier which is capable of recognizing object(fishes) which provided in the training set "n01440764".

I have until now created two folders containing the training data, and validation data. 

I have then resized the images to be 256x256. 

According to the tutorial is the next step to compute the image mean using  "./examples/imagenet/make_imagenet_mean.sh"

Problem is though when i try to run the script, it terminates with the error message:

user@ubuntu:~/caffe/examples/imagenet$ ./make_imagenet_mean.sh terminate called after throwing an instance of 'std::bad_alloc' what(): std::bad_alloc Aborted (core dumped) Done.

this is how the script looks like on my computer.

#!/usr/bin/env sh
# Compute the mean image from the imagenet training lmdb
# N.B. this is available in data/ilsvrc12


EXAMPLE=/home/user/caffe/examples/imagenet
DATA=/home/user/caffe/data/ilsvrc12
TOOLS=/home/user/caffe/build/tools


$TOOLS/compute_image_mean $EXAMPLE/ilsvrc12_train_lmdb \
  $DATA/imagenet_mean.binaryproto


echo "Done."

I have no idea why get that message, it seems like one those messages which you get when the code is stuck in an infinite loop or something like that..  

I would be very happy if someone could clarify what I am doing wrong..

[陶信东]

I ran into this too. It turns out that the image size should be 256*256 if you use the default model.

[Jalen Hawkins]

I am running into the same issue did you ever resolve the problem?

 ./examples/imagenet/make_imagenet_mean.sh

[Erika Nixon]

I am having the same problem.

I am in the process of running a script to check the image sizes.

So far, this does not seem to be the problem.

Have you found a solution?

Thanks!

-Erika

[mscoder17]

How did you resolve the issue?? I am having exactly same issue

[Jonathan R. Williford]

std::bad_alloc often means that you are running out of memory. Is this possible?

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