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currency_detector
Ironman1612/currency_detector
currency_detector is a machine learning model from Ironman1612. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
A lightweight currency detection model trained using the Ultralytics YOLO architecture for assistive technology applications such as EyeCure, a system designed to help visually impaired users identify Indian currency…
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Updated Mar 11, 2026
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At a glance
- Access
- Public
- Created
- Mar 11, 2026
- Updated
- Mar 11, 2026
- SHA
- 0709dcbc
Model card
From the Hugging Face model README
Currency Detection Model
A lightweight currency detection model trained using the Ultralytics YOLO architecture for assistive technology applications such as EyeCure, a system designed to help visually impaired users identify Indian currency notes using a camera.
Model Details
- Architecture: YOLO26n
- Framework: Ultralytics YOLO
- Task: Object Detection
- Classes:
| Class |
|---|
| ₹10 |
| ₹20 |
| ₹50 |
| ₹100 |
| ₹200 |
| ₹500 |
The model is optimized for real-time inference and lightweight deployment on edge devices.
Training Configuration
-
Epochs: 100
-
Image Size: 640
-
Batch Size: 16
-
Dataset Split:
- Train: 70%
- Validation: 20%
- Test: 10%
Training was performed on a GPU (Tesla T4).
Evaluation Metrics
Overall validation performance:
| Metric | Value |
|---|---|
| Precision | 0.977 |
| Recall | 0.926 |
| mAP@50 | 0.966 |
| mAP@50-95 | 0.868 |
Per-Class Performance
| Currency | Precision | Recall | mAP@50 | mAP@50-95 |
|---|---|---|---|---|
| ₹10 | 0.990 | 0.915 | 0.982 | 0.935 |
| ₹20 | 0.972 | 0.957 | 0.980 | 0.889 |
| ₹50 | 0.990 | 0.977 | 0.993 | 0.898 |
| ₹100 | 0.959 | 0.930 | 0.961 | 0.800 |
| ₹200 | 0.990 | 0.933 | 0.978 | 0.901 |
| ₹500 | 0.959 | 0.845 | 0.903 | 0.784 |
Inference Speed
| Stage | Time |
|---|---|
| Preprocess | 0.3 ms |
| Inference | 2.8 ms |
| Postprocess | 0.6 ms |
This enables real-time detection in live camera applications.
Example Use
from ultralytics import YOLO
model = YOLO("currency_model.pt")
results = model("currency_image.jpg")
results.show()
Use Case
This model was developed as part of the EyeCure assistive system, which provides:
- Real-time currency recognition
- Voice feedback for visually impaired users
- Offline edge deployment
Limitations
- Performance may drop in extremely low lighting conditions.
- Detection accuracy may decrease for heavily folded or partially visible notes.
Future Improvements
- Improve multi-currency detection in a single frame
- Expand dataset with real-world scenarios
Author
Abdul Saboor AIML Engineering Student EyeCure Project
- Created
- Mar 11, 2026
- Updated
- Mar 11, 2026