Indian Currency Classification

Image Classification of new Indian Currency Notes using Keras and TensorFlow

A model has a life-cycle, and this very simple knowledge provides the backbone for both modeling a dataset and understanding the tf.keras API.

• The five steps in the life-cycle are as follows:

  1. Define the model
  2. Compile the model
  3. Fit the model
  4. Evaluate the model
  5. Make predictions

Classes

• No. of Classes - 7
• Name of Classes - 10, 20, 50, 100, 200, 500, 2000

Dataset

• Custom Dataset
• Resizing Images to (300,300,3) in the local machine using Icecream Image Resizer
• No. of (training + validation) images - 1239
• No. of test images - 55

Preprocessing of Data

• Resizing images to (128, 128, 3)
• Splitting data
  1. Training - 992 images
  2. Validation - 247 images
• Normalizing the images
• Shuffling the training dataset
• Interpolation - Bicubic

Important Packages

• TensorFlow
• Keras
• Numpy
• Matplotlib

Model Specifications

• Sequential model built from scratch
• No. of layers used - 18
• Activation Function - ReLU, Softmax
• Optimization Algorithm - Adam
• Learning Rate - 0.0001
• Loss Function - SparseCategoricalCrossentropy

Model Architecture

Evaluation of Model on the Validation Dataset

• Validation:

  1. loss: 0.030041895806789398
  2. accuracy: 0.9959514141082764

Inference Results on the Test Set