Datasetpublic

ebowwa/usd-side-coco-annotations

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… See the full description on the dataset page: https://huggingface.co/datasets/ebowwa/usd-side-coco-annotations.

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Dataset Card

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 - "Front/Back of USD 2" project.

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

License

MIT