| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: |
| image: |
| mode: L |
| splits: |
| - name: train |
| num_bytes: 1236712550 |
| num_examples: 670 |
| download_size: 1236834019 |
| dataset_size: 1236712550 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: mit |
| task_categories: |
| - image-segmentation |
| size_categories: |
| - n<1K |
| --- |
| # Apple Segmentation Minnesota |
|
|
| A dataset for semantic segmentation of Apple Segmentation Minnesota. The dataset contains 670 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 |
| @misc{hani2019minneapple, |
| title={MinneApple: A Benchmark Dataset for Apple Detection and Segmentation}, |
| author={Nicolai Häni and Pravakar Roy and Volkan Isler}, |
| year={2019}, |
| eprint={1909.06441}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV} |
| } |
| ``` |
|
|
| https://rsn.umn.edu/projects/orchard-monitoring/minneapple |
