Why can't I classify the last class?

[fengpingsh]

I used a dataset which have 40 person and each have 10 pictures, and I did the fine tuning based on the VGG 16 module. It was quite good then fine-tuning. Here is the result:

I1222 04:02:16.620232  4708 data_layer.cpp:73] Restarting data prefetching from start.
I1222 04:02:47.203390  4703 solver.cpp:397]     Test net output #0: accuracy = 0.99
I1222 04:02:47.203533  4703 solver.cpp:397]     Test net output #1: loss = 3.71212 (* 1 = 3.71212 loss)
I1222 04:02:47.203552  4703 solver.cpp:315] Optimization Done.
I1222 04:02:47.203562  4703 caffe.cpp:259] Optimization Done.

When I doing the testing(classification), I found each picture was correctly classified but the last one. Should I set the num_output in train_val_deploy.prototxt to 41(I didn't noticed anyone who have done in that way)? It really confused me.

[Thomio-Watanabe]

You have 10 images of 40 different people (400 images total) and each person is a class, right ?
Your output should be 40, like you said.
I suggest you double check how you are feeding the network during train and test phases.
Maybe you are using one class (person) several times.

[139419qw]

@fengpingbaustem ，Hello, I'm also working with fine-tuning VGGFACE, But I got low accuracy. Can I contact u for further communication?

[duygusar]

Is VGGFace handling the dataset the same way Caffe would? E.g. labels should start from 0 to 9 for 10 classes.

[fengpingsh]

@139419qw sure. @duygusar I think that's the point, I set the label start from 1 :-(

[duygusar]

@fengpingbaustem Sounds like that is the problem, you should rearrange your data according to Caffe. You can refer to some guides for preparing your own dataset from scratch for Caffe.

[Noiredd]

Closing as it looks like the issue is solved. In case you need further assistance, please post on the caffe-users list.