---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: objects
    struct:
    - name: bbox
      list:
        list: float64
    - name: categories
      list:
        class_label:
          names:
            '0': weed
  splits:
  - name: train
    num_bytes: 12796353
    num_examples: 358
  download_size: 12800061
  dataset_size: 12796353
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- n<1K
---
# Paddy Weed Target Detection

This dataset provides real-world RGB images captured in rice paddy fields during the seedling stage, focusing on the detection of weeds within rice crops under natural field conditions. Images were collected using a handheld Canon IXUS 1000 HS camera with a telephoto lens, offering close-up field-level perspectives for agricultural computer vision applications. The dataset contains 358 images with 840 bounding box annotations across 1 category.

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

## Citation

```bibtex
@article{deng2023weed,
  title={Weed target detection at seedling stage in paddy fields based on YOLOX},
  author={Deng, Xiangwu and Qi, Long and Liu, Zhuwen and Liang, Song and Gong, Kunsong and Qiu, Guangjun},
  journal={PLOS ONE},
  volume={18},
  pages={e0294709},
  year={2023},
  publisher={Public Library of Science}
}
```


*This dataset was reformatted from its original format to match HuggingFace standards.*