Multiclass image classification of Bark-50 texture data from https://www.kaggle.com/datasets/saurabhshahane/barkvn50
Created more images for classes which had less number of images than the average number images of all classes uisng image augmentaion. Reference for the augmentation technique: https://github.com/mdbloice/Augmentor/blob/master/notebooks/Per_Class_Augmentation_Strategy.ipynb
Loaded the pre-trained Resnet101V2 (without the top level feature layes) and added GlobalPooling2D, Dropout and final dense layer to complete the model architecture.
Average accuracy on the training set: 98%
Average accuracy on the validation set: 90%