Hello, I was testing out a sample project of classifying digit 0 and digit 1 from mnist dataset. I could not see class 0 in my feature generation and I am struggling to find an issue. I think it may be a bug with the uploader or the way it handles files. Could you please look into my project and see if I made an error or is it a bug?
Hi Louis, thank you for looking into and forwarding it to appropriate team. I wanted to check if you have any update with this issue and if there is something I need to do with the dataset that I am uploading?
I have just sent a new message to check if they had time to have a look.
I don’t think it is a blocker for your project although I understand it is annoying I can see that you managed to train your NN (with a good accuracy).
I’ll come back to you as soon I have more information.
Thanks for looking into this again. Unfortunately, I think it is a blocker. If you notice the result graph that it generates, I can only see one sample of the class 0, which means that it is trying to classify between >5000 samples of class 1 and a single sample of class 0. Notice also the loss and accuracy are very high that I start to get from the beginning due to this extremely high imbalance in this classes.
I am not sure @rjames had time yet to have a deeper look.
On your project I cloned on my side, I tried different things:
I exported your data, deleted them all, import them again -> Same error
I changed the RGB color depth to Greyscale -> Same error
Changed the Image processing block to Raw data -> I can detect clusters
You only see samples from the one class because the Live Classification page limits the ** Classify existing test sample** dropdown to 1000 samples and your one class contains more than 1000 samples you don’t see the other class.
You can still grab samples from (any, other) class by going to Model Testing page, select classify all. Then you’ll be able to scroll to any sample, click on the ... options menu, choose Show classification result. You’ll be taken to the Live Classification page with the sample selected and thus will be able to see the features.