Indian Currency Notes Classifier

[aaradhyasinghgaur]

🛠️ Related Issue

• Closes Indian Currency Notes Classifier #255

📝 Description

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.

🔍 Type of PR

• 🐛 Bug fix
• ✨ Feature enhancement
• 📚 Documentation update
• 🛑 Other (specify): Addiition of a project

📸 Screenshots / 🎥 Videos (if applicable)

[Attach screenshots or videos]

✅ Checklist

• I have performed a self-review of my code.
• I have read and followed the Contribution Guidelines.
• I have tested the changes thoroughly before submitting this pull request.
• I have provided relevant issue numbers, screenshots, and videos after making the changes.
• I have commented my code, particularly in hard-to-understand areas.

ℹ️ Additional Context

[Include any additional context or information that might assist reviewers in understanding the changes]

@Akshat111111 kindly review my pr and add suitable level (Level-3) if possible because I have trained the dataset using 5 different models and compared their accuracy using performance matrices and also used data-augmentation and added custom layers which takes a lot of time and computational resources.

[github-actions]

Welcome to Our repository.🎊 Thank you so much for taking the time to point this out.

[Akshat111111]

Excellent work!

[github-actions]

🎉 Your pull request has been successfully merged! 🎉 Thank you for your contribution to our project. Your efforts are greatly appreciated. Keep up the fantastic work! 🚀