This model is built using fastai library and tarined on a kaggle dataset.
The data set is a collection of images of alphabets from the American Sign Language, separated in 29 folders which represent the various classes.
The training data set contains 87,000 images which are 200x200 pixels. There are 29 classes, of which 26 are for the letters A-Z and 3 classes for SPACE, DELETE and NOTHING. These 3 classes are very helpful in real time applications, and classification. The test data set contains a mere 29 images, to encourage the use of real world test images
The dataset is trained on resent34 and resnet50 can managed to get and accuracy of 99.9%
