[Project Addition] : Indian Currency Detection using DL

[aaradhyasinghgaur]

Pull Request for DL-Simplified 💡

Issue Title : Indian Currency Detection using DL

• Info about the related issue (Aim of the project) : To classify indian currency for ease in conditions like poor lighting or for individuals with visual impairments
• Name: Aaradhya Singh
• GitHub ID: https://github.com/kyra-09
• Email ID: [email protected]
• Idenitfy yourself: (Mention in which program you are contributing in. Eg. For a JWOC 2022 participant it's, JWOC Participant) GSSOC-2024 Contributor

Closes: #709

Describe the add-ons or changes you've made 📃

1. Utilizing Multiple Network Architectures:

To classify diffrent current 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.
we 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, I 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.

4. 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.

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

Give a clear description of what have you added or modifications made

Type of change ☑️

What sort of change have you made:

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Code style update (formatting, local variables)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

How Has This Been Tested? ⚙️

Describe how it has been tested
Describe how have you verified the changes made

Checklist: ☑️

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

[github-actions]

Our team will soon review your PR. Thanks @kyra-09 :)

[abhisheks008]

Approved @kyra-09