| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: int64 |
| - name: categories |
| list: |
| class_label: |
| names: |
| '0': grape |
| '1': '1' |
| splits: |
| - name: train |
| num_bytes: 48707920 |
| num_examples: 448 |
| download_size: 48689074 |
| dataset_size: 48707920 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| # Grape Detection Syntheticday |
|
|
| A dataset for object detection of synthetic grape bunches. The dataset contains 448 images with 8,828 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{10.3389/fpls.2019.01185, |
| AUTHOR={Bailey, Brian N.}, |
| TITLE={Helios: A Scalable 3D Plant and Environmental Biophysical Modeling Framework}, |
| JOURNAL={Frontiers in Plant Science}, |
| VOLUME={10}, |
| YEAR={2019}, |
| URL={https://www.frontiersin.org/article/10.3389/fpls.2019.01185}, |
| DOI={10.3389/fpls.2019.01185}, |
| ISSN={1664-462X} |
| } |
| ``` |
