| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: float64 |
| - name: categories |
| list: |
| class_label: |
| names: |
| '0': '0' |
| - name: split |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 120289152 |
| num_examples: 1212 |
| download_size: 120727429 |
| dataset_size: 120289152 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - object-detection |
| size_categories: |
| - 1K<n<10K |
| --- |
| # Grapevine Growth Detection |
|
|
| A dataset for detection of grapevine bunches. The dataset contains 1,212 images with 3,476 bounding box annotations across 1 category. |
|
|
| 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{schieck2023comparison, |
| title={Comparison of deep learning methods for grapevine growth stage recognition}, |
| author={Schieck, Martin and Krajsic, Philippe and Loos, Felix and Hussein, Abdulbaree and Franczyk, Bogdan and Kozierkiewicz, Adrianna and Pietranik, Marcin}, |
| journal={Computers and Electronics in Agriculture}, |
| volume={211}, |
| pages={107944}, |
| year={2023}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
| Schieck, M. (2022, October 31). Grapevine development stages (BBCH 71-79). Kaggle. https://www.kaggle.com/datasets/schieckmartin/grapesdevelopmentstages |
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
