| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| splits: |
| - name: train |
| num_bytes: 22742227 |
| num_examples: 224 |
| download_size: 22744687 |
| dataset_size: 22742227 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| task_categories: |
| - image-segmentation |
| size_categories: |
| - n<1K |
| --- |
| |
| # Rice Seedling Segmentation |
|
|
| A dataset for semantic segmentation of Rice Seedling Segmentation. The dataset contains 224 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{electronics9101602, |
| AUTHOR = {Khan, Abbas and Ilyas, Talha and Umraiz, Muhammad and Mannan, Zubaer Ibna and Kim, Hyongsuk}, |
| TITLE = {CED-Net: Crops and Weeds Segmentation for Smart Farming Using a Small Cascaded Encoder-Decoder Architecture}, |
| JOURNAL = {Electronics}, |
| VOLUME = {9}, |
| YEAR = {2020}, |
| NUMBER = {10}, |
| ARTICLE-NUMBER = {1602}, |
| URL = {https://www.mdpi.com/2079-9292/9/10/1602}, |
| ISSN = {2079-9292}, |
| DOI = {10.3390/electronics9101602} |
| } |
| ``` |
|
|
| https://github.com/kabbas570/CED-Net-Crops-and-Weeds-Segmentation-for-Smart-Farming-Using-a-Small-Cascaded-Encoder-Decoder-Archi |
