Instance-segmentation model for the "hang the wire" hook mission of the SAE Brasil Eletroquad 2026 competition, trained by Black Bee Drones. It segments two classes - rose (each visible segment of the suspended rope) and sphere (the orange sphere on the rope) - and runs on every control-loop tick of the mission. The team finished 2nd overall (official results).

Base model: yolo26n-seg.pt (Ultralytics)
Task: instance segmentation
Classes: rose (0), sphere (1)
Input size: 960
Dataset: blackbeedrones/sae-2026-hook

Mission

The drone takes off, finds the orange sphere mounted on one of two suspended ropes, parks a fixed 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. Because each visible rope segment is its own rose instance, the controller can measure both rope arms and choose a side. The mission runs on a Jetson Orin Nano through Nectar SDK.

Results

Evaluated on the dataset's test split (395 images, 501 instances), NMS iou=0.6. The numbers below are Ultralytics-native (COCO 101-point interpolation); see SUMMARY.md for the full breakdown, including the SDK/torchmetrics figures (which run a different curve discretization and read systematically lower at mAP@50-95).

Metric	Box	Mask
mAP@50	0.9929	0.9844
mAP@50-95	0.9640	0.8535

Per-class mAP@50-95:

Class	Box	Mask
`rose`	0.9450	0.7555
`sphere`	0.9831	0.9515

Operating-point precision / recall / F1 at the per-class optimal confidences (from the F1 curve):

Class	conf	Box P / R / F1	Mask P / R / F1
`rose`	0.47	0.963 / 0.975 / 0.969	0.946 / 0.958 / 0.952
`sphere`	0.70	0.987 / 0.993 / 0.990	0.987 / 0.993 / 0.990

Recommended inference settings

Setting	Value
`imgsz`	960
`iou` (NMS)	0.6
`conf` (`rose`)	0.47
`conf` (`sphere`)	0.70

Predict at the lower of the two confidences and apply a per-class filter, so each class keeps its own threshold.

Usage

Nectar SDK

python

from nectar.ai.segmentation import Segmentor
from nectar.ai.detection.postprocess import PerClassConfidenceFilter

# loads weights/best.pt from the Hub
segmentor = Segmentor("blackbeedrones/sae-2026-hang-all-yolo26n-seg-v2-960")
segmentor.load()

result = segmentor.segment(image, conf=0.47, iou=0.6, imgsz=960)

# rose=0, sphere=1
per_class = PerClassConfidenceFilter(threshold_mapping={0: 0.47, 1: 0.70}, default_threshold=0.47)
kept = per_class.filter(result.to_supervision())
for seg in result:
    print(seg.class_name, f"{seg.confidence:.2f}", f"area={seg.mask_area}px")

Ultralytics

python

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download("blackbeedrones/sae-2026-hang-all-yolo26n-seg-v2-960", "weights/best.pt")
model = YOLO(weights)
results = model.predict("image.jpg", imgsz=960, iou=0.6, conf=0.47)

Training

Trained with Ultralytics through the Nectar SDK on blackbeedrones/sae-2026-hook (Roboflow sae-2026-hang v2: 9,235 train / 396 val / 395 test images). Full configuration in experiment.config.yaml.

Parameter	Value
Base model	`yolo26n-seg.pt`
Image size	960
Batch size	48
Epochs	up to 250, early-stopped at 165 (patience 20)
LR / schedule	0.01, linear with cosine (`cos_lr`), `lrf=0.01`
Weight decay	5e-4
Warmup	3 epochs
EMA	on (decay 0.9997)
Augmentation	mosaic 1.0 (closed last 10 epochs), HSV, fliplr 0.5, scale 0.5, translate 0.1
Mask	`overlap_mask=true`, `mask_ratio=4`
Seed	42

Files

weights/best.pt - recommended checkpoint (best validation).
weights/last.pt, weights/epoch*.pt - last and per-epoch checkpoints.
evaluation/ - curves, confusion matrix, prediction samples, per-class CSV/JSON.
experiment.config.yaml, args.yaml, results.csv - training configuration and log.
SUMMARY.md - full evaluation breakdown and threshold analysis.

blackbeedrones/sae-2026-hang-all-yolo26n-seg-v2-960

SAE Eletroquad 2026 - Hook segmentation (YOLO26n-seg)