Datasets:
File size: 1,501 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
- name: split
dtype: string
splits:
- name: train
num_bytes: 29494844
num_examples: 788
download_size: 29526452
dataset_size: 29494844
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
task_categories:
- image-segmentation
size_categories:
- n<1K
---
# Cwf788 Weed Segmentation
This dataset provides real-world RGB imagery for semantic segmentation of weeds in agricultural fields. It captures diverse weed instances under natural field conditions, offering a practical resource for developing and evaluating computer vision models in precision agriculture applications. The dataset contains 788 images with pixel-level mask annotations.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
The original train/test/val split has been preserved in the `split` column.
## Citation
```bibtex
@article{li2019real,
title={Real-time crop recognition in transplanted fields with prominent weed growth: a visual-attention-based approach},
author={Li, Nan and Zhang, Xiaoguang and Zhang, Chunlong and Guo, Huiwen and Sun, Zhe and Wu, Xinyu},
journal={IEEE Access},
volume={7},
pages={185310--185321},
year={2019},
publisher={IEEE}
}
```
https://github.com/ZhangXG001/Real-Time-Crop-Recognition
*This dataset was reformatted from its original format to match HuggingFace standards.* |