README.md

metadata

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 dataset with 2,179 samples.

The images and original annotations are from PhenoBench, 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 (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.

License

CC BY-SA 4.0, inherited from the upstream PhenoBench release.

Installation

pip install -U fiftyone

Usage

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:

@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 for the authoritative citation and licensing terms.