README.md

---
license: cc-by-4.0
tags:
- synthetic-data
- object-detection
- computer-vision
- agriculture
- apple-detection
- benchmark
- yolov8
- domain-randomization
language: en
task_categories:
- object-detection
pretty_name: ApplesM5 Synthetic Apple Detection Benchmark
configs:
  - config_name: default
    data_files:
      - split: train
        path: "real-original/yolos/images/trains/*.jpg"
      - split: validation
        path: "real-original/yolos/images/vals/*.jpg"
---

# 🍎 ApplesM5: Synthetic Apple Detection Benchmark

This repository hosts the data files (images and annotations) used in the Synetic AI research paper, **"Better Than Real: Synthetic Apple Detection for Orchards."** This dataset was created through procedural content generation and physically-based rendering (PBR) to provide a clean, highly generalized training signal for robust agricultural AI.

The data demonstrates that training exclusively on this synthetic dataset yields superior generalization compared to models trained solely on real-world data, achieving up to a **+34.24% increase in mAP50-95**.

## Dataset Structure and Format

The dataset is provided in a file-based structure optimized for training YOLO models.

| Split | Description | Format | Total File Count |
| :--- | :--- | :--- | :--- |
| `train/` | Synthetic, procedurally generated images and labels. (Used for training.) | YOLOv8 (1 class) | > 10,000 |
| `val/` | Real-world image samples from external orchards. (Used for validation/testing.) | YOLOv8 (1 class) | ~300 |

## Citation

Please cite the associated whitepaper when using this dataset in your research:

```bibtex
@article{synetic2025applesm5,
  title={{Better Than Real: Synthetic Apple Detection for Orchards}},
  author={Blaga, Octavian and Scott, David and Zand, Ramtin and Seekings, James Blake},
  journal={ResearchGate preprint},
  year={2025},
  doi={10.13140/RG.2.2.29696.49920},
  url={https://www.researchgate.net/publication/397341880_Better_Than_Real_Synthetic_Apple_Detection_for_Orchards},
  note={Code available at: \url{https://github.com/Syneticai/ApplesM5}}
}