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---
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
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
---
<details><summary>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 |
</details> |