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--- |
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license: agpl-3.0 |
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base_model: |
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- Ultralytics/YOLO11 |
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pipeline_tag: image-classification |
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datasets: |
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- Rokyuto/Banknotes |
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tags: |
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- yolov11 |
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- banknotes |
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- banknotes classification |
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widget: |
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- text: Banknotes Classification |
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output: |
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url: model_predictions/prediction_50 EUR_20240923_190943.jpg |
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model-index: |
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- name: banknotes-recognizer |
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results: |
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- task: |
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type: object-classification |
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metrics: |
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- type: precision |
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name: Precision |
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value: 0.976 |
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- type: recall |
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name: Recall |
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value: 0.974 |
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- type: mAP50 |
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name: mAP50 |
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value: 0.991 |
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- type: mAP50-95 |
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name: mAP50-95 |
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value: 0.789 |
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--- |
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<details><summary>Metrics</summary> |
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YOLO11m summary (fused): 303 layers, 20,037,742 parameters, 0 gradients, 67.7 GFLOPs |
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| Class | Images | Instances | Box(P) | R | mAP50 | mAP50-95 | |
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|--------|--------|-----------|---------|-------|-------|----------| |
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| all | 110 | 256 | 0.969 | 0.977 | 0.989 | 0.801 | |
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| 5 BGN | 10 | 35 | 0.969 | 0.9 | 0.975 | 0.712 | |
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| 10 BGN | 9 | 29 | 0.96 | 1 | 0.976 | 0.773 | |
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| 20 BGN | 7 | 25 | 0.996 | 0.96 | 0.993 | 0.795 | |
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| 50 BGN | 7 | 24 | 0.996 | 0.966 | 0.989 | 0.801 | |
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| 100 BGN| 13 | 41 | 0.975 | 0.955 | 0.982 | 0.823 | |
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| 5 EUR | 18 | 19 | 0.863 | 0.991 | 0.986 | 0.837 | |
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| 10 EUR | 14 | 38 | 0.998 | 1 | 0.995 | 0.787 | |
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| 20 EUR | 15 | 15 | 0.986 | 1 | 0.995 | 0.861 | |
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| 50 EUR | 7 | 7 | 0.97 | 1 | 0.995 | 0.920 | |
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| 100 EUR| 10 | 23 | 0.97 | 1 | 0.995 | 0.675 | |
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</details> |
