vdd-cabinet-mobilenetv3-large

CABiNet (MobileNetV3-Large) Finetuned on VDD

CABiNet (MobileNetV3-Large backbone) 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.

<p align="center"> <img src="demo_grid.png" alt="CABiNet (MobileNetV3-Large) on VDD: Input / Ground Truth / Prediction"> </p>

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

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Performance

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VDD Model Zoo

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Per-Class IoU (%)

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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

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Usage

Install Dependencies

pip install torch huggingface_hub
git clone https://github.com/dronefreak/CABiNet.git
cd CABiNet
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e .[dev]

Load Model from Hugging Face

from huggingface_hub import hf_hub_download
import torch

from src.models.cabinet import CABiNet

weights = hf_hub_download(
    repo_id="dronefreak/vdd-cabinet-mobilenetv3-large",
    filename="cabinet_best.pth"
)

model = CABiNet(n_classes=7, mode="large")
ckpt = torch.load(weights, map_location="cpu", weights_only=True)
state_dict = ckpt["model_state"] if isinstance(ckpt, dict) and "model_state" in ckpt else ckpt
model.load_state_dict(state_dict)
model.eval()

Run Inference

import torch

image = ...  # (1, 3, H, W) normalized float tensor
with torch.no_grad():
    logits = model(image)[0]
mask = logits.argmax(dim=1).squeeze(0).cpu().numpy()  # (H, W) class-ID map

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Training Configuration

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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

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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!

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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

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Citation

Please cite the following:

@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}
}