I have gone through the first two lectures of part 1 course. So i started a project which is handwriting classification for tamil alphabets. There are 156 classes and I have about 60000 images of different handwriting images (about 250 - 350 images per class. When I ran resnet and used techniques like unfreeze and lr_find, I get an accuracy of 91% which is pretty cool. I wanted to increase the accuracy rate and so i looked at the alphabets where there were errors.
I found the alphabets which had errors. I have given two examples here. These are four classes alphabets which were misclassified about 30 times each. As you can see class 1 and 2 are somewhat similar and if some cropping happened in 2, it will look like class 1. So my doubt is maybe some cropping or some kind of transformation of images is happening. All the images in my dataset are of different sizes. I have used this command to collect the images as a databunch.
data = ImageDataBunch.from_name_re(path, fnames, pat, size = 224, bs=bs,
ds_tfms=get_transforms(do_flip=False, max_rotate=0, max_zoom=1, max_lighting=0, max_warp=0, flip_vert=False, p_affine=1, p_lighting=1)
Any suggestions appreciated