Millions of people around the world have low or no vision. Assistive software applications have been developed for a variety of day-to-day tasks, including currency recognition. To aid with this task, we present BankNote-Net, an open dataset for assistive currency recognition. The dataset consists of a total of 24,816 embeddings of banknote images captured in a variety of assistive scenarios, spanning 17 currencies and 112 denominations. These compliant embeddings were learned using supervised contrastive learning and a MobileNetV2 architecture, and they can be used to train and test specialized downstream models for any currency, including those not covered by our dataset or for which only a few real images per denomination are available (few-shot learning). We deploy a variation of this model for public use in the last version of the Seeing AI app developed by Microsoft, which has over a 100 thousand monthly active users.
If you make use of this dataset or pre-trained model in your own project, please consider referencing this GitHub repository and citing our paper:
@article{oviedoBankNote-Net2022,
title = {BankNote-Net: Open Dataset for Assistive Currency Recognition},
author = {Felipe Oviedo, Srinivas Vinnakota, Eugene Seleznev, Hemant Malhotra, Saqib Shaikh & Juan Lavista Ferres},
journal = {https://arxiv.org/pdf/2204.03738.pdf},
year = {2022},
}
The dataset data structure consists of 256-dimensional vector embeddings with additional columns for currency, denomination and face labels, as explained in the data exploration notebook. The dataset is saved as 24,826 x 258 flat table in feather and csv file formats. Figure 1 presents some of these learned embeddings.
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Install requirements.
Please, use the conda environment file env.yaml to install the right dependencies.
# Create conda environment conda create env -f env.yaml # Activate environment to run examples conda activate banknote_net -
Example 1: Train a shallow classifier directly from the dataset embeddings for a currency available in the dataset. For inference, images should be encoded first using the keras MobileNet V2 pre-trained encoder model.
Run the following file from root: train_from_embedding.py
python src/train_from_embedding.py --currency AUD --bsize 128 --epochs 25 --dpath ./data/banknote_net.featherusage: train_from_embedding.py [-h] --currency {AUD,BRL,CAD,EUR,GBP,INR,JPY,MXN,PKR,SGD,TRY,USD,NZD,NNR,MYR,IDR,PHP} [--bsize BSIZE] [--epochs EPOCHS] [--dpath DPATH] Train model from embeddings. optional arguments: -h, --help show this help message and exit --currency {AUD,BRL,CAD,EUR,GBP,INR,JPY,MXN,PKR,SGD,TRY,USD,NZD,NNR,MYR,IDR,PHP}, --c {AUD,BRL,CAD,EUR,GBP,INR,JPY,MXN,PKR,SGD,TRY,USD,NZD,NNR,MYR,IDR,PHP} String of currency for which to train shallow classifier --bsize BSIZE, --b BSIZE Batch size for shallow classifier --epochs EPOCHS, --e EPOCHS Number of epochs for training shallow top classifier --dpath DPATH, --d DPATH Path to .feather BankNote Net embeddings -
Example 2: Train a classifier on top of the BankNote-Net pre-trained encoder model using images in a custom directory. Input images must be of size 224 x 224 pixels and have square aspect ratio. For this example, we use a couple dozen images spanning 8 classes for Swedish Krona, structured as in the example_images/SEK directory, that contains both training and validation images.
Run the following file from root: train_custom.py
python src/train_custom.py --bsize 4 --epochs 25 --data_path ./data/example_images/SEK/ --enc_path ./models/banknote_net_encoder.h5usage: train_custom.py [-h] [--bsize BSIZE] [--epochs EPOCHS] [--data_path DATA_PATH] [--enc_path ENC_PATH] Train model from custom image folder using pre-trained BankNote-Net encoder. optional arguments: -h, --help show this help message and exit --bsize BSIZE, --b BSIZE Batch size --epochs EPOCHS, --e EPOCHS Number of epochs for training shallow top classifier. --data_path DATA_PATH, --data DATA_PATH Path to folder with images. --enc_path ENC_PATH, --enc ENC_PATH Path to .h5 file of pre-trained encoder model. -
Example 3: Perform inference using the SEK few-shot classifier of Example 2, and the validation images on example_images/SEK/val
Run the following file from root: predict_custom.py, returns encoded predictions.
python src/predict_custom.py --bsize 1 --data_path ./data/example_images/SEK/val/ --model_path ./src/trained_models/custom_classifier.h5usage: predict_custom.py [-h] [--bsize BSIZE] [--data_path DATA_PATH] [--model_path MODEL_PATH] Perform inference using trained custom classifier. optional arguments: -h, --help show this help message and exit --bsize BSIZE, --b BSIZE Batch size --data_path DATA_PATH, --data DATA_PATH Path to custom folder with validation images. --model_path MODEL_PATH, --enc MODEL_PATH Path to .h5 file of trained classification model.
Copyright (c) Microsoft Corporation. All rights reserved.
The dataset is open for anyone to use under the CDLA-Permissive-2.0 license. The embeddings should not be used to reconstruct high resolution banknote images.
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