| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: int64 |
| - name: categories |
| list: |
| class_label: |
| names: |
| '0': mango |
| splits: |
| - name: train |
| num_bytes: 78222025 |
| num_examples: 1242 |
| download_size: 77966427 |
| dataset_size: 78222025 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| # Mango Detection Australia |
|
|
| A dataset for object detection of mangoes. The dataset contains 1,242 images with 10,619 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 |
| @Misc{Koirala2019, |
| author={Koirala, Anand and Walsh, Kerry and Wang, Z. and McCarthy, C.}, |
| title={MangoYOLO data set}, |
| year={2019}, |
| month={2021}, |
| day={10-19}, |
| publisher={Central Queensland University}, |
| url={https://figshare.com/articles/dataset/MangoYOLO_data_set/13450661, https://researchdata.edu.au/mangoyolo-set} |
| } |
| ``` |
|
|
|
|
