Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,70 @@
+---
+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**: 7,360
+- **Format**: COCO (object detection)
+- **Classes**: 33 (denominations × front/back × authentic/counterfeit)
+
+| Split | Images |
+|-------|--------|
+| Train | 5,290 |
+| Valid | 1,221 |
+| Test | 849 |
+
+## Classes
+
+### Denomination + Side (18 classes)
+- `100USD-Front`, `100USD-Back`
+- `50USD-Front`, `50USD-Back`
+- `20USD-Front`, `20USD-Back`
+- `10USD-Front`, `10USD-Back`
+- `5USD-Front`, `5USD-Back`
+- `1USD-Front`, `1USD-Back`
+
+### Counterfeit Detection (15 classes)
+- Counterfeit versions for each denomination
+
+## Annotation Refinement
+
+This dataset was refined using Roboflow's `usd-classification/1` model to reclassify generic labels (e.g., `100USD`) into specific front/back variants:
+
+- **2,236 annotations** auto-reclassified
+- **97% success rate** on classification
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ebowwa/usd-side-coco-annotations")
+```
+
+Or download the zip directly and extract for use with YOLO/RF-DETR training.
+
+## Source
+
+Original dataset from [Roboflow](https://app.roboflow.com) - "Front/Back of USD" project.
+
+## License
+
+MIT