| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| - name: split |
| dtype: string |
| - name: date |
| dtype: string |
| - name: time |
| dtype: string |
| - name: lat |
| dtype: string |
| - name: lon |
| dtype: string |
| - name: utc |
| dtype: string |
| - name: weather |
| dtype: string |
| - name: wind |
| dtype: string |
| - name: seeding_date |
| dtype: string |
| - name: height_mm |
| dtype: string |
| - name: depth |
| dtype: image |
| - name: depth_refined |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 9013751981 |
| num_examples: 2568 |
| download_size: 8747620007 |
| dataset_size: 9013751981 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-4.0 |
| task_categories: |
| - image-segmentation |
| size_categories: |
| - 1K<n<10K |
| --- |
| # We3ds Weed Segmentation |
|
|
| This dataset provides real-world RGB imagery focused on weed segmentation within agricultural fields. It captures diverse weed and crop patterns under natural field conditions, offering a practical resource for developing and evaluating semantic segmentation models in precision agriculture applications. The dataset contains 2,568 images with pixel-level mask annotations. |
|
|
| 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{kitzler2023we3ds, |
| title={WE3DS: An RGB-D image dataset for semantic segmentation in agriculture}, |
| author={Kitzler, Florian and Barta, Norbert and Neugschwandtner, Reinhard W and Gronauer, Andreas and Motsch, Viktoria}, |
| journal={Sensors}, |
| volume={23}, |
| number={5}, |
| pages={2713}, |
| year={2023}, |
| publisher={MDPI} |
| } |
| ``` |
|
|
| Kitzler, F., Barta, N., Neugschwandtner, R. W., Gronauer, A., & Motsch, V. (2023). WE3DS: An RGB-D image dataset for semantic segmentation in agriculture [Dataset]. In Sensors (Vol. 23, Issue 5, p. 2713). Zenodo. https://doi.org/10.5281/zenodo.7457983 |
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
|
|
