Indian Currency Notes Classification

[aaradhyasinghgaur]

Closes :- #779 Approach I'd taken :- 1. Utilizing Multiple Network Architectures:

To classify different currency notes such as - 1)Ten Rupee Notes 2)Twenty Rupee Notes 3)Fifty Rupee Notes 4)Hundred Rupee Notes 5)Two Hundred Rupee Notes 6)Five Hundred Rupee Notes, and, 7)Two Thousand Rupee Notes. I will employ five distinct deep learning network architectures:

• DenseNet121
• Xception
• VGG16
• ResNet50
• InceptionV3

2. Data Augmentation Techniques: To enhance the accuracy and robustness of the models, we will apply various data augmentation techniques such as:

• Rotation
• Zooming
• Flipping (horizontal and vertical)
• Shearing
• Brightness adjustments

These techniques will artificially expand the dataset and help prevent overfitting. 3. Model Performance Comparison: I will evaluate and compare the performance of each model using the following metrics and visualizations:

• Accuracy Score: To measure the overall correctness of the models.
• Loss Graph: To visualize the loss during training and validation phases.
• Accuracy Graph: To track accuracy improvements over epochs.
• Confusion Matrix: To provide a detailed breakdown of model performance across different diamond shapes, highlighting precision, recall, and F1 score for each category.

5. Exploratory Data Analysis (EDA): Before training the models, I will perform comprehensive exploratory data analysis (EDA) on the dataset to understand its structure. This will include:

• Distribution of images across different diamond shapes.
• Image quality and resolution consistency.
• Identifying any class imbalances.
• Visualizing sample images from each category.

6. README File: A README file will be created to document the entire process according to the READMe template.

@Niketkumardheeryan @invigorzz313 kindly review my pr and assign suitable lablel (Level-3) for it if possible cause I have trained the dataset using 5 different models , used data-augmentation techniques to increase the accuracy of models in various conditions , added custom layesr , done EDA analysis which takes a lot of time and computational resources.

In case of no problems kindly merge it to the repo sir . thank you for your time.