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RoboND_Project_Robotic_Inference

The Project explores an embedded robotics inference system, leveraging Nvidia’s Deep Learning tools, to create models on a DIGITS workflow platform that can be deployed in real time robotics applications. Real time image analysis and recognition of different features of Indian Bank Currency Notes– Original and Fake, using GoogLeNet neural network has been proposed.

Data Acquisition

The Data Acquisition details in Training and Validation Image set can be represented as below:

Supplied Dataset

Collected INR Dataset

Results

The results on Accuracy and Inference Time for supplied dataset:

Supplied Dataset Accuracy

Following curve shows the training loss, validation loss and accuracy levels for the INR Currency banknotes datasets, with a GoogLeNet architecture:

INR Banknotes Dataset Accuracy

The prediction on test dataset with the trained classification model accurately determines all the test images with over 90% accuracy levels. The resulting predictions on test dataset is shown below:

Prediction on INR Banknotes Test Dataset

Hence we have our own Inference system for recognizing bank notes, one of the vital areas of research to prevent banknotes forgery, theft and currency frauds !!

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