---
license: agpl-3.0
base_model:
- Ultralytics/YOLO11
pipeline_tag: image-classification
datasets:
- Rokyuto/Banknotes
tags:
- yolov11
- banknotes
- banknotes classification
widget:
  - text: Banknotes Classification
    output:
      url: model_predictions/prediction_50 EUR_20240923_190943.jpg
model-index:
- name: banknotes-recognizer
  results:
  - task:
      type: object-classification
    dataset:
      type: banknotes
      name: Banknotes
    metrics:
    - type: precision
      name: Precision
      value: 0.976
    - type: recall
      name: Recall
      value: 0.974
    - type: mAP50
      name: mAP50
      value: 0.991
    - type: mAP50-95
      name: mAP50-95
      value: 0.789
---

<div align="center">
  <img width="80%" src="model_predictions/prediction_50 EUR_20240923_190943.jpg" alt="Output Example">
</div>

<details open><summary>Model Metrics</summary>

YOLO11m summary (fused): 303 layers, 20,037,742 parameters, 0 gradients, 67.7 GFLOPs

| Class  | Images | Instances |  Box(P) |   R   | mAP50 | mAP50-95) |
|--------|--------|-----------|---------|-------|-------|----------|
| all    |  110   |   256     |  0.969  | 0.977 | 0.989 |  0.801   |
| 5 BGN  |  10    |   35      |  0.969  |  0.9   | 0.975 |  0.712   |
| 10 BGN |  9     |   29      |  0.96   |   1    | 0.976 |  0.773   |
| 20 BGN |  7     |   25      |  0.996  | 0.96  | 0.993 |  0.795   |
| 50 BGN |  7     |   24      |  0.996  | 0.966 | 0.989 |  0.801   |
| 100 BGN|  13    |   41      |  0.975  | 0.955 | 0.982 |  0.823   |
| 5 EUR  |  18    |   19      |  0.863  | 0.991 | 0.986 |  0.837   |
| 10 EUR |  14    |   38      |  0.998  |   1    | 0.995 |  0.787   |
| 20 EUR |  15    |   15      |  0.986  |   1    | 0.995 |  0.861   |
| 50 EUR |  7     |   7       |  0.97   |   1    | 0.995 |  0.920   |
| 100 EUR|  10    |   23      |  0.97   |   1    | 0.995 |  0.675   |

<div align="center">
  <img width="80%" src="model_performance/results.png" alt="Results">
  <img width="80%" src="model_performance/confusion_matrix_normalized.png" alt="Confusion Matrix Normalized">
  <img width="80%" src="model_performance/labels.jpg" alt="Labels">
  <img width="80%" src="model_performance/F1_curve.png" alt="F1 curve">
  <img width="80%" src="model_performance/P_curve.png" alt="P curve">
  <img width="80%" src="model_performance/R_curve.png" alt="R curve">
  <img width="80%" src="model_performance/PR_curve.png" alt="PR curve">
</div>


</details>