| --- |
| 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.* |
