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