label lmdb not right

[yayong-guan]

when i created lmdb follow the code ,i get the error : innernum == bottom[1]->count() (1 vs. 20) Number of labels must match number of predictions; 20 is the number of classes, it show the label lmbd is not right, can not create one image with 20 lables.

[sukritshankar]

Please check that the data you are inputting to the create_label_lmdb.py is in the format N x 20 (where N is the number of training images). Also, have you changed the prototxt appropriately, where the num_output in the fc8 layer should be 20 !! Other than this, I dont see why this might happen !! Hope this helps !!

[yayong-guan]

thanks for your answer. yeah, the label is in the format N x 20, and each line value is [0, 1, 0, 1, ..., 1, 0], length is 20, where 1 means belongs to the index class, 0 not, like this: [[ 1. 0. 0. ..., 0. 0. 0.] [ 0. 0. 0. ..., 0. 0. 0.] [ 0. 0. 0. ..., 0. 0. 0.] ..., [ 0. 0. 0. ..., 0. 1. 0.] [ 1. 0. 0. ..., 0. 0. 0.] [ 0. 0. 1. ..., 0. 0. 0.]] (1540, 20)

then the num_output in the fc8 layer is 20

the create_label_lmdb.py core code is ：

[sukritshankar]

This looks correct and should not give the error that you are mentioning !! However please make sure that you are not using a single label loss like softmax but a multiple label loss like sigmoid cross entropy !!

[sukritshankar]

Also I think you should input labels in [0,255] for creating label LMDB (see README) as caffe rescales the values by 255 !! Your current label feed will not cause any error but will effect training !!

[yayong-guan]

I use the SigmoidCrossEntropyLoss, so that is why i doubt！i did not use your create_data_lmdb.py, instead of caffe own convert_imageset, is that influences？？

[sukritshankar]

That should not affect in case you are using two data layers, one for data and one for labels as shown in the attached prototxt !!

[yayong-guan]

thanks, i will try it again!

[yayong-guan]

i got why, the accuracy style is wrong

you konw how to test the accuracy when train like above?

[sukritshankar]

For training I reckon phase should be TRAIN !!