| --- |
| annotations_creators: |
| - expert-generated |
| language: en |
| license: cc-by-sa-4.0 |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - object-detection |
| - image-segmentation |
| task_ids: |
| - semantic-segmentation |
| - instance-segmentation |
| pretty_name: PhenoBench |
| tags: |
| - agriculture |
| - agtech |
| - crops |
| - drone |
| - fiftyone |
| - image |
| - image-segmentation |
| - instance-segmentation |
| - object-detection |
| - phenotyping |
| - semantic-segmentation |
| - sugar-beet |
| - uav |
| - weeds |
| --- |
| |
|  |
|
|
| # Dataset Card for PhenoBench |
|
|
| This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2,179 samples. |
|
|
| The images and original annotations are from [PhenoBench](https://www.phenobench.org/), |
| a large UAV image dataset for semantic image interpretation in the agricultural |
| domain (sugar beet crops and weeds), introduced by Weyler et al. in |
| [*PhenoBench — A Large Dataset and Benchmarks for Semantic Image Interpretation |
| in the Agricultural Domain*](https://arxiv.org/abs/2306.04557) (IEEE TPAMI, |
| 2024). This release packages a subset of PhenoBench as a FiftyOne dataset and |
| adds object-detection predictions and embeddings produced by Voxel51. |
|
|
| ## What's in this dataset |
|
|
| - **2,179 samples** (train: 1,407 / val: 386 / test: 386), each a 1024×1024 RGB |
| UAV image of a sugar beet field |
| - **Original PhenoBench annotations** (from phenobench.org): |
| - `semantics` — semantic segmentation: background, crop, weed, partial-crop, |
| partial-weed |
| - `plant_instances` — per-plant instance segmentation |
| - `leaf_instances` — per-leaf instance segmentation |
| - `plant_visibility`, `leaf_visibility` — visibility heatmaps |
| - **Voxel51-added detections and embeddings**: |
| - `yolo11n`, `yolo11l` — YOLO11 (nano and large) object detection predictions |
| for plants, with per-detection true-positive / false-positive / |
| false-negative matches against the ground-truth instance masks |
| - Brain runs in `brain/`: CLIP and DINOv2 embeddings + similarity indexes for |
| sample-level and patch-level visual search |
| |
| For the full original PhenoBench dataset (train/val/test = 1,407 / 772 / 693) |
| and the canonical annotation specification, see [phenobench.org](https://www.phenobench.org/). |
|
|
| ## License |
|
|
| CC BY-SA 4.0, inherited from the upstream PhenoBench release. |
|
|
| ## Installation |
|
|
| ```bash |
| pip install -U fiftyone |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| import fiftyone as fo |
| from fiftyone.utils.huggingface import load_from_hub |
| |
| # Load the dataset |
| dataset = load_from_hub("Voxel51/PhenoBench") |
| |
| # Launch the FiftyOne App |
| session = fo.launch_app(dataset) |
| ``` |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the original PhenoBench paper: |
|
|
| ```bibtex |
| @article{weyler2024phenobench, |
| title={PhenoBench: A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain}, |
| author={Weyler, Jan and Magistri, Federico and Marks, Elias and Chong, Yue Linn and Sodano, Matteo and Roggiolani, Gianmarco and Chebrolu, Nived and Stachniss, Cyrill and Behley, Jens}, |
| journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, |
| year={2024}, |
| url={https://arxiv.org/abs/2306.04557} |
| } |
| ``` |
|
|
| Please refer to [phenobench.org](https://www.phenobench.org/) for the |
| authoritative citation and licensing terms. |
|
|
