| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| splits: |
| - name: train |
| num_bytes: 92768863 |
| num_examples: 125 |
| download_size: 92773297 |
| dataset_size: 92768863 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: gpl-3.0 |
| task_categories: |
| - image-segmentation |
| size_categories: |
| - n<1K |
| --- |
| |
|
|
| # Sugarbeet Weed Segmentation |
|
|
| A dataset for semantic segmentation of Sugarbeet Weed Segmentation. The dataset contains 125 images with pixel-level mask annotations. |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @ARTICLE{8115245, |
| author={I. Sa and Z. Chen and M. Popović and R. Khanna and F. Liebisch and J. Nieto and R. Siegwart}, |
| journal={IEEE Robotics and Automation Letters}, |
| title={weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming}, |
| year={2018}, |
| volume={3}, |
| number={1}, |
| pages={588-595}, |
| doi={10.1109/LRA.2017.2774979}, |
| month={Jan} |
| } |
| ``` |
|
|
| https://github.com/inkyusa/weedNet |
