/Indian-Currency-Classification

Image Classification of new Indian Currency Notes using Keras and TensorFlow. Build model from scratch.

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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