---
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

```python
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](https://app.roboflow.com/ds/OWTzlaSd01?key=LTqYj5YlFh) - "Front/Back of USD 2" project.

Refined using automated Roboflow classification API with incremental saving for fault tolerance.

## License

MIT