| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': '0' |
| '1': '1' |
| '2': '2' |
| '3': '3' |
| '4': '4' |
| '5': '5' |
| '6': '6' |
| '7': '7' |
| '8': '8' |
| - name: species |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 489520348 |
| num_examples: 17509 |
| download_size: 492631617 |
| dataset_size: 489520348 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| size_categories: |
| - 10K<n<100K |
| --- |
| # Deepweeds Classification |
|
|
| This dataset comprises real-world RGB images capturing various weed species in agricultural field environments. Collected under natural outdoor conditions, the images provide a diverse visual representation of weeds for computer vision applications in precision agriculture. The dataset contains 17,509 images across 9 classes: 0, 1, 2, 3, 4, 5, 6, 7, 8. |
| Images per class: |
| - 0: 1,125 |
| - 1: 1,064 |
| - 2: 1,031 |
| - 3: 1,022 |
| - 4: 1,062 |
| - 5: 1,009 |
| - 6: 1,074 |
| - 7: 1,016 |
| - 8: 9,106 |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{olsen2019deepweeds, |
| title={DeepWeeds: A multiclass weed species image dataset for deep learning}, |
| author={Olsen, Alex and Konovalov, Dmitry A and Philippa, Bronson and Ridd, Peter and Wood, Jake C and Johns, Jamie and Banks, Wesley and Girgenti, Benjamin and Kenny, Owen and Whinney, James and others}, |
| journal={Scientific reports}, |
| volume={9}, |
| number={1}, |
| pages={2058}, |
| year={2019}, |
| publisher={Nature Publishing Group UK London} |
| } |
| ``` |
|
|
| https://github.com/AlexOlsen/DeepWeeds |
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
|
|
