cabinet-mobilenetv3-large-uavid

CABiNet (MobileNetV3-Large) Finetuned on UAVid

CABiNet (MobileNetV3-Large backbone) semantic segmentation model for aerial UAV imagery using the UAVid benchmark dataset.

This model is part of the UAVid 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="uavid_showcase.gif" alt="UAVid Semantic Segmentation Demo"> </p>

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Performance

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UAVid Model Zoo

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Per-Class IoU (%)

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Dataset

UAVid is a high-resolution UAV semantic segmentation benchmark of urban street scenes, captured from oblique aerial viewpoints along street-side flight paths.

Classes

• Clutter
• Building
• Road
• Static Car
• Tree
• Vegetation
• Human
• Moving Car

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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/cabinet-mobilenetv3-large-uavid",
    filename="cabinet_best.pth"
)

model = CABiNet(n_classes=8, 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

• UAVid Semantic Segmentation Model Zoo: https://huggingface.co/collections/dronefreak/uavid-semantic-segmentation-model-zoo
• CABiNet repository: https://github.com/dronefreak/CABiNet
• CABiNet Paper: https://arxiv.org/abs/2011.00993v2
• Official UAVid Website: https://uavid.nl/
• UAVid Dataset Archive: https://doi.org/10.17026/dans-x9f-w9sa
• UAVid Paper: https://arxiv.org/abs/1810.10438
• UAVid Published Journal: https://doi.org/10.1016/j.isprsjprs.2020.05.009
• 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:

• Very small or thin objects (e.g. pedestrians, moving cars at altitude)
• Heavy occlusion under tree canopy
• Motion blur on moving vehicles
• Mixed/very high input resolutions (UAVid source images are 3840x2160 / 4096x2160; both pipelines evaluate at reduced imgsz)

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Citation

Please cite the following:

@article{LYU2020108,
    author = "Ye Lyu and George Vosselman and Gui-Song Xia and Alper Yilmaz and Michael Ying Yang",
    title = "UAVid: A semantic segmentation dataset for UAV imagery",
    journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
    volume = "165",
    pages = "108 - 119",
    year = "2020",
    issn = "0924-2716",
    doi = "https://doi.org/10.1016/j.isprsjprs.2020.05.009",
    url = "http://www.sciencedirect.com/science/article/pii/S0924271620301295",
}

@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}
}