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dronefreak/
vdd-yolo26n-sem

Name: vdd-yolo26n-sem
Availability: InStock

ultralytics

semantic-segmentation

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YOLO26n-sem Finetuned on VDD

Fine-tuned YOLO26n semantic segmentation model for aerial drone imagery using the VDD (Varied Drone Dataset) benchmark dataset.

This model is part of the VDD Semantic Segmentation Model Zoo, a collection of CABiNet and YOLO26 models trained and evaluated under a common pipeline for aerial semantic segmentation.

YOLO26n-sem on VDD: Input / Ground Truth / Prediction

_{Qualitative results on VDD test-split examples — single-scale (imgsz=1024) inference, no TTA.}

Performance

Metric	Score
mIoU	73.99
Pixel Accuracy	86.32
Parameters (M)	1.63
FLOPs (GFLOPs @ 1024px)	11.4

VDD Model Zoo

Rank	Model	mIoU (%)	Pixel Acc (%)	Params (M)	FLOPs (GFLOPs)
1	YOLO26x-sem	78.83	89.79	40.16	430.9
2	YOLO26l-sem	78.57	89.68	17.87	192.4
3	CABiNet (MobileNetV3-Large)	77.76	89.57	9.17	54.8
4	YOLO26m-sem	77.02	88.3	14.32	152.3
5	YOLO26s-sem	76.35	88.27	6.50	44.4
6	YOLO26n-sem	73.99	86.32	1.63	11.4

Per-Class IoU (%)

Class	YOLO26x-sem	YOLO26l-sem	CABiNet (MobileNetV3-Large)	YOLO26m-sem	YOLO26s-sem	YOLO26n-sem
Other	64.15	65.88	66.28	61.04	60.99	57.27
Wall	69.26	70.93	65.87	70.06	67.48	64.76
Road	72.61	72.18	70.2	70.78	69.68	67.78
Vegetation	90.14	89.68	91.06	89.59	88.63	85.01
Vehicle	70.99	68.52	73.38	68.76	66.08	62.96
Roof	89.34	87.53	86.0	84.52	85.83	84.86
Water	95.3	95.29	91.54	94.41	95.79	95.33

Evaluation Visualizations

Per-Class IoU Bar Chart

Confusion Matrix

Loss Curves

Dataset

VDD (Varied Drone Dataset) is a semantic segmentation benchmark for drone imagery spanning varied altitudes, viewpoints, and scenes (urban, rural, and natural), captured at a uniform native resolution of 4000x3000.

Classes

Other
Wall
Road
Vegetation
Vehicle
Roof
Water

Usage

Install Dependencies

pip install ultralytics huggingface_hub

Load Model from Hugging Face

python

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download(
    repo_id="dronefreak/vdd-yolo26n-sem",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

python

results = model.predict(source="image.png", task="semantic", imgsz=1024)
mask = results[0].semantic_mask.cpu().numpy().data  # (H, W) class-ID map

Training Configuration

Setting	Value
Epochs	150
Image size	1024
Batch size	4
Dataset	VDD (converted images/+masks/ format)
Framework	Ultralytics YOLO
cls_pw (class weighting)	0.5

Official Resources

VDD Semantic Segmentation Model Zoo: https://huggingface.co/collections/dronefreak/vdd-semantic-segmentation-model-zoo
CABiNet repository: https://github.com/dronefreak/CABiNet
CABiNet Paper: https://arxiv.org/abs/2011.00993v2
VDD Dataset (Hugging Face): https://huggingface.co/datasets/RussRobin/VDD
VDD Repository: https://github.com/RussRobin/VDD
VDD Paper (arXiv): https://arxiv.org/abs/2305.13608
VDD Published (JVCIR): https://www.sciencedirect.com/science/article/pii/S1047320325000434
Ultralytics YOLO: https://github.com/ultralytics/ultralytics
Ultralytics YOLO26 Paper: https://arxiv.org/abs/2606.03748

Training Framework

Trained with the CABiNet repository, which pairs its own real-time segmentation trainer with a parallel Ultralytics YOLO26-sem pipeline — shared dataset tooling, training/eval, and mIoU benchmarking across UAVid, AeroScapes, and VDD. Star the repo if you find these models useful!

Known Limitations

Performance may degrade in:

Small training set (280 images) — heavier augmentation (mosaic/mixup/copy-paste) offsets this during training, but rare-class generalization may still be limited
Rare classes (Vehicle, Roof, Water) are underrepresented relative to Vegetation/Road/Wall
Very high native resolution (4000x3000, uniform) downsampled to the eval imgsz — fine detail on small objects (e.g. vehicles at altitude) can be lost
Varied altitude/viewpoint scenes (the dataset's defining trait) can shift the domain between training crops and a given inference image

Citation

Please cite the following:

bibtex

@article{cai2025vdd,
  title={Vdd: Varied drone dataset for semantic segmentation},
  author={Cai, Wenxiao and Jin, Ke and Hou, Jinyan and Guo, Cong and Wu, Letian and Yang, Wankou},
  journal={Journal of Visual Communication and Image Representation},
  volume={109},
  pages={104429},
  year={2025},
  publisher={Elsevier}
}

@INPROCEEDINGS{9560977,
  author={Kumaar, Saumya and Lyu, Ye and Nex, Francesco and Yang, Michael Ying},
  booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
  title={CABiNet: Efficient Context Aggregation Network for Low-Latency Semantic Segmentation},
  year={2021},
  pages={13517-13524},
  doi={10.1109/ICRA48506.2021.9560977}
}

@article{Kumaar_Real-time_Semantic_Segmentation_2021,
  author = {Kumaar, Saumya and Lyu, Ye and Nex, Francesco and Yang, Michael Ying},
  doi = {10.1016/j.isprsjprs.2021.06.006},
  journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
  pages = {124--134},
  title = {{Real-time Semantic Segmentation with Context Aggregation Network}},
  url = {https://www.sciencedirect.com/science/article/pii/S0924271621001647},
  volume = {178},
  year = {2021}
}

@article{jocher2026ultralytics,
  title={Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models},
  author={Jocher, Glenn and Qiu, Jing and Liu, Mengyu and Lyu, Shuai and Akyon, Fatih Cagatay and Kalfaoglu, Muhammet Esat},
  journal={arXiv preprint arXiv:2606.03748},
  year={2026}
}

@software{cabinet_uavid_benchmark,
  author = {Kumaar, Saumya},
  title = {CABiNet: Semantic Segmentation Benchmarking on UAVid (CABiNet vs. YOLO26)},
  url = {https://github.com/dronefreak/CABiNet},
  year = {2026}
}