README.md

---
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- 1K<n<10K
language:
- en
tags:
- soyabean
- soyabean-pod
- object-detection
---

# YOLOPOD — YOLO Soybean Pod Object Detection Dataset

A ground-level imagery dataset for soybean pod detection and counting. The dataset contains 1,448 images with 72,709 annotated pod instances (single class: `pod`). Images are collected from two field locations (Chongzhou and Renshou). Bounding boxes are in COCO format `[x, y, width, height]`.

| Split | Images | Annotations |
|-------|--------|-------------|
| train | 1,448 | 72,709 |

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

## Loading

```python
from datasets import load_dataset

ds = load_dataset("Project-AgML/YOLOPOD_Soyabean_Pod_Counting_Dataset")
```

# Citation

```bibtex
@article{xiang2023yolopod,
  title={YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting},
  author={Xiang, Shuning and Wang, Sijun and Xu, Min and Wang, Wei and Liu, Wei},
  journal={Plant Methods},
  volume={19},
  number={1},
  year={2023},
  publisher={Springer},
  doi={10.1186/s13007-023-00985-4}
}

Xiang, Shuning; Wang, Sijun; Xu, Min; Wang, Wei; Liu, Wei (2023), "YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting", Plant Methods, 19, doi: 10.1186/s13007-023-00985-4
```