Hi,
This post is not specifically about a pytorch related issue but a general computer vision problem and I hope this is the correct place for such a discussion.
I am trying to classify a rather messy dataset of images. The images are taken using mobile phones and scanner scans from various sources so there is some variation in the dataset.
Its a dataset of election party flyers/leaflets, there are about 5 classes and each class/label has about 3000 images associated with it so there is enough variation here. Here are a couple examples of what the images look like:
leaflet_1041_11024×1400 223 KB
leaflet_100_21449×1024 204 KB
leaflet_1139_21024×1437 183 KB
leaflet_1015_11024×1096 185 KB
As a starting point, I used a ResNet 101 network to learn this dataset and for preprocessing all I did was resize the images to 200 x 200 using interpolation.
But after training for over 24 hours the validation/test accuracy is about 54%, so the network did not lean much and is just random guessing.
Is it possible to classify such a dataset? Any tips on how to pre-process these images to improve result?
Thanks