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.gitattributes

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README.md

@@ -1,3 +1,42 @@
 ---
 license: cc-by-4.0
+task_categories:
+  - object-detection
+size_categories:
+  - 1K<n<10K
 ---
+
+# 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
+```

annotations.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eb799c9cdf278e36bc9e6542081611aaa85c9088fcf67f5346f5cd9e53869c93
+size 18425122