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--- |
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license: mit |
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task_categories: |
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- object-detection |
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language: |
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- en |
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tags: |
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- currency |
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- USD |
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- money-detection |
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- COCO |
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- RF-DETR |
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- YOLO |
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size_categories: |
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- 1K<n<10K |
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--- |
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# USD Side Detection Dataset (Front/Back) |
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A refined COCO-format dataset for detecting US Dollar currency and classifying whether the **front** or **back** side is visible. |
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## Dataset Summary |
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- **Total Images**: 3,618 |
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- **Total Annotations**: 3,746 |
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- **Format**: COCO + HuggingFace JSONL |
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- **Classes**: 24 (denominations × front/back × authentic/counterfeit) |
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- **Classification Accuracy**: 100% (all Front/Back classified) |
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| Split | Images | Annotations | |
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|-------|--------|-------------| |
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| Train | 2,671 | 2,738 | |
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| Valid | 597 | 627 | |
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| Test | 350 | 381 | |
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## Class Mapping (24 classes) |
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| ID | Class | ID | Class | |
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|---|-------|---|-------| |
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| 0 | 100USD-Back | 12 | Counterfeit 100 USD Back | |
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| 1 | 100USD-Front | 13 | Counterfeit 100 USD Front | |
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| 2 | 10USD-Back | 14 | Counterfeit 10USD Back | |
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| 3 | 10USD-Front | 15 | Counterfeit 10USD Front | |
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| 4 | 1USD-Back | 16 | Counterfeit 1USD Back | |
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| 5 | 1USD-Front | 17 | Counterfeit 1USD Front | |
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| 6 | 20USD-Back | 18 | Counterfeit 20USD Back | |
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| 7 | 20USD-Front | 19 | Counterfeit 20USD Front | |
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| 8 | 50USD-Back | 20 | Counterfeit 50USD Back | |
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| 9 | 50USD-Front | 21 | Counterfeit 50USD Front | |
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| 10 | 5USD-Back | 22 | Counterfeit 5USD Back | |
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| 11 | 5USD-Front | 23 | Counterfeit 5USD Front | |
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**Breakdown:** |
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- **12 Regular USD**: Front/Back for $1, $5, $10, $20, $50, $100 |
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- **12 Counterfeit USD**: Front/Back for $1, $5, $10, $20, $50, $100 |
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**Note**: All $2 bills and generic annotations removed - only Front/Back classified data remains. |
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## Annotation Refinement |
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This dataset was refined using Roboflow's `usd-classification/1` model: |
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### Phase 1: Regular USD ✅ |
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- Reclassified 2,236 generic labels to Front/Back variants |
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- **97% success rate** |
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### Phase 2: Counterfeit USD ✅ |
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- Reclassified 943 counterfeit annotations across all splits (train/valid/test) |
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- **97.8% success rate** (269/275 in valid/test, 674/762 in train) |
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- Only 13 annotations remain generic (SSL errors during classification) |
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### Phase 3: Data Cleaning ✅ |
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- Removed 289 $2 bill annotations (146 regular + 143 counterfeit) |
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- Reason: Model lacks "two-front"/"two-back" classes, generalization only 75% accurate |
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## Final Statistics |
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- **3,746 annotations** - 100% classified to Front/Back |
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- **24 classes** - 12 regular + 12 counterfeit |
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- **0 $2 bills** (all 289 removed - 146 regular + 143 counterfeit) |
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## Usage |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("ebowwa/usd-side-coco-annotations") |
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``` |
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Or download directly and extract for use with YOLO/RF-DETR training. |
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## Source |
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Original dataset from [Roboflow](https://app.roboflow.com/ds/OWTzlaSd01?key=LTqYj5YlFh) - "Front/Back of USD 2" project. |
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Refined using automated Roboflow classification API with incremental saving for fault tolerance. |
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## License |
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MIT |
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