ML-capsule is a Project for beginners and experienced data science Enthusiasts who don't have a mentor or guidance and wish to learn Machine learning. Using our repo they can learn ML, DL, and many related technologies with different real-world projects and become Interview ready.
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
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.
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.
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.
README File:
A README file will be created to document the entire process according to the READMe template.
Solution :- 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
Rotation
Zooming
Flipping (horizontal and vertical)
Shearing
Brightness adjustments
These techniques will artificially expand the dataset and help prevent overfitting.
Model Performance Comparison: I will evaluate and compare the performance of each model using the following metrics and visualizations:
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:
Dataset I'll use :- https://www.kaggle.com/datasets/shobhit18th/indian-currency-notes
@Niketkumardheeryan @invigorzz313 kindly assign this issue to me.