Datasets:

blackbeedrones
/

sae-2026-hook

license: apache-2.0
task_categories:
  - image-segmentation
  - object-detection
tags:
  - image-segmentation
  - instance-segmentation
  - drone
  - uav
  - robotics
  - sae-eletroquad
size_categories:
  - 10K<n<100K
pretty_name: SAE Eletroquad 2026 - Hook (Hang the Wire)
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: image_id
      dtype: int64
    - name: width
      dtype: int32
    - name: height
      dtype: int32
    - name: objects
      struct:
        - name: id
          sequence: int64
        - name: bbox
          sequence:
            sequence: float32
            length: 4
        - name: category
          sequence:
            class_label:
              names:
                '0': rose
                '1': sphere
        - name: area
          sequence: float64
        - name: segmentation
          sequence:
            sequence: float32

SAE Eletroquad 2026 - Hook (Hang the Wire)

Instance-segmentation dataset for the "hang the wire" hook mission of the SAE Brasil Eletroquad 2026 competition, collected and annotated by Black Bee Drones. Two classes: rose (each visible segment of the suspended rope) and sphere (the orange sphere mounted on the rope).

Trained model: blackbeedrones/sae-2026-hang-all-yolo26n-seg-v2-960 — a YOLO26n-seg model trained and evaluated on this exact version of the data.

Competition

SAE Brasil Eletroquad is a student competition for autonomous quadrotors. The 2026 edition ran on 14–17 May 2026 at Univap (São José dos Campos, SP). Black Bee Drones finished 2nd overall (official results).

Mission

In the hook mission the drone takes off, finds the orange sphere mounted on one of two suspended ropes, parks a fixed real-world distance from it, picks which side of the rope to fly along, turns perpendicular to the rope, descends on LIDAR, releases a hook with a servo, and lands. A single YOLO-seg model runs on every control-loop tick: each visible rope segment is its own rose instance, which is what lets the controller choose a side. The mission code is the hook ROS 2 package, built on Nectar SDK.

Classes

id	name	description
0	`rose`	A single visible segment of the suspended rope/wire. Several instances can appear per image.
1	`sphere`	The orange sphere on the rope; the mission's primary visual anchor.

Dataset structure

Split	Images	`rose`	`sphere`	Instances
train	9,235	7,787	3,402	11,189
validation	396	355	154	509
test	395	354	147	501
Total	10,026	8,496	3,703	12,199

Each row is an image plus an objects struct with one entry per instance:

bbox — axis-aligned box [x_min, y_min, width, height] in absolute pixels, computed from the polygon.
area — polygon area in pixels.
category — ClassLabel (rose / sphere).
segmentation — a flat polygon [x1, y1, x2, y2, ...] in absolute pixels (one polygon per instance).

Image dimensions are stored per row in width / height. The Hub viewer draws the bounding boxes; the polygons are kept for mask training.

Provenance

Exported from the Roboflow project black-bee-drones/sae-2026-hang (version 2) and mirrored here. This is the version the published model was trained and evaluated on — its test split (395 images, 501 instances) matches the model's reported evaluation.

Usage

Load with HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("blackbeedrones/sae-2026-hook")
example = dataset["train"][0]
print(example["objects"]["category"], example["objects"]["segmentation"][0][:8])

Materialize as YOLO-seg (for training)

from nectar.ai.segmentation.datasets import HuggingFaceSegHandler

handler = HuggingFaceSegHandler("data/sae-2026-hook")
handler.download(repo_id="blackbeedrones/sae-2026-hook", format_type="yolo")
# data/sae-2026-hook now has data.yaml + {train,valid,test}/{images,labels}

Or from the command line:

nectar-ai segment dataset download --source huggingface \
  --repo blackbeedrones/sae-2026-hook --format yolo --output data/sae-2026-hook

Train with Nectar SDK

from nectar.ai.segmentation import Segmentor, SegTrainingConfig

segmentor = Segmentor("yolo26n-seg.pt")
segmentor.load()
segmentor.train(SegTrainingConfig(dataset_path="data/sae-2026-hook/data.yaml", epochs=100, imgsz=960))

The published model runs at imgsz=960, iou=0.6, with per-class confidence thresholds rose=0.47 and sphere=0.70.

References

Trained model: blackbeedrones/sae-2026-hang-all-yolo26n-seg-v2-960
Nectar SDK
SAE Brasil Eletroquad
Eletroquad 2026 official results