Pakistan currency classification using deep learning, The model achieved a classification accuracy of 98.5%.
The link to gist of the PKRS_Classifier.ipynb
The link to github PKRS_Classifier.ipynb
This code is based on code from a fast.ai MOOC that will be publicly available in Jan 2019.
I wanted to see how accurate a Neural Network would be at categorising images of currency. Being a resident of Pakistan i wanted to train it on the Pakistnai Rupee but a quick google search discovered that there were very little camera images of the different Pakistani notes on the internet so i created the dataset myself.
The different bank notes in the dataset are as following :
I took the 6 images below from this blog post, images in the dataset are my own.
The first step was to find as many currency notes as i could, everyone in the family lent me their riches for science!
I then took pictures of all the notes and divided them in train and validation set, the distribution of the dataset is as following :
| Category | Train | Valid | Total |
|---|---|---|---|
| ten | 101 | 44 | 145 |
| twenty | 12 | 6 | 20 |
| fifty | 15 | 7 | 22 |
| hundred | 14 | 6 | 20 |
| five_hundred | 2 | 2 | 4 |
| Thousand | 5 | 3 | 8 |
| TOTAL | 149 | 68 | 217 |
| Percentage | 68.7% | 31.3% | 100% |
The data was distributed into a dataset directory structure as shown below:
