--- license: agpl-3.0 pipeline_tag: image-segmentation library_name: ultralytics base_model: "Ultralytics/YOLO26" tags: - semantic-segmentation - aerial-imagery - drone - uavid - yolo26 - ultralytics - pytorch - computer-vision datasets: - dronefreak/UAVid-2020 metrics: - miou - pixel-accuracy --- # YOLO26m-sem Finetuned on UAVid ![License](https://img.shields.io/badge/License-AGPL--3.0-1f6feb?style=flat-square) ![Framework](https://img.shields.io/badge/Framework-Ultralytics-6a5acd?style=flat-square) ![Dataset](https://img.shields.io/badge/Dataset-UAVid-0aa1a7?style=flat-square) ![mIoU](https://img.shields.io/badge/mIoU-61.98%25-e8a33d?style=flat-square) ![Status](https://img.shields.io/badge/Status-Trained-2ea44f?style=flat-square) ![Maintained](https://img.shields.io/badge/Maintained-yes-17a2b8?style=flat-square) Fine-tuned YOLO26m 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.

UAVid Semantic Segmentation Demo

--- ## Performance | Metric | Score | | ------------------- | --------------- | | mIoU | 61.98 | | Pixel Accuracy | 84.41 | | Parameters (M) | 14.32 | | FLOPs (GFLOPs @ 1024px) | 152.3 | --- ## UAVid Model Zoo | Rank | Model | mIoU (%) | Pixel Acc (%) | Params (M) | FLOPs (GFLOPs) | | ---- | --------------------- | ------------- | ------------------ | ----------------- | ----------------- | | 1 | CABiNet (MobileNetV3-Large) | 68.6 | 87.31 | 9.17 | 54.8 | | 2 | CABiNet (MobileNetV3-Small) | 66.84 | 86.45 | 5.36 | 44.1 | | 3 | YOLO26x-sem | 64.41 | 85.82 | 40.16 | 430.9 | | 4 | YOLO26l-sem | 63.28 | 84.75 | 17.87 | 192.4 | | 5 | YOLO26m-sem | 61.98 | 84.41 | 14.32 | 152.3 | | 6 | YOLO26s-sem | 61.69 | 84.27 | 6.50 | 44.4 | | 7 | YOLO26n-sem | 58.17 | 82.31 | 1.63 | 11.4 | --- ## Per-Class IoU (%) | Class | CABiNet (MobileNetV3-Large) | CABiNet (MobileNetV3-Small) | YOLO26x-sem | YOLO26l-sem | YOLO26m-sem | YOLO26s-sem | YOLO26n-sem | | --- | --- | --- | --- | --- | --- | --- | --- | | Clutter | 69.37 | 67.96 | 67.34 | 65.63 | 64.63 | 63.8 | 61.46 | | Building | 87.77 | 86.59 | 87.37 | 85.9 | 86.1 | 85.01 | 82.29 | | Road | 81.62 | 80.94 | 79.82 | 78.87 | 78.61 | 78.14 | 75.25 | | Static Car | 59.3 | 55.69 | 51.33 | 54.48 | 44.74 | 47.39 | 41.08 | | Tree | 81.23 | 80.09 | 78.19 | 76.68 | 76.44 | 76.8 | 74.06 | | Vegetation | 65.88 | 64.12 | 63.3 | 59.97 | 60.07 | 60.25 | 55.65 | | Human | 30.04 | 27.32 | 21.09 | 19.34 | 20.41 | 18.88 | 15.82 | | Moving Car | 73.62 | 71.99 | 66.81 | 65.38 | 64.88 | 63.3 | 59.73 | --- ## Evaluation Visualizations ### Per-Class IoU Bar Chart ![IoU Bar Chart](iou_bar_chart.png) ### Confusion Matrix ![Confusion Matrix](confusion_matrix_normalized.png) ### Loss Curves ![Loss Curves](results.png) --- ## Dataset [UAVid](https://uavid.nl/) 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 --- ## Usage ### Install Dependencies ```bash 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/uavid-yolo26m-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 | 500 | | Image size | 1024 | | Batch size | 8 | | Dataset | UAVid (converted images/+masks/ format) | | Framework | Ultralytics YOLO | | cls_pw (class weighting) | 0.5 | --- ## 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 --- ## Training Framework Trained with the [CABiNet repository](https://github.com/dronefreak/CABiNet), which pairs its own real-time segmentation trainer with a parallel Ultralytics YOLO26-sem pipeline — shared UAVid dataset tooling, training/eval, and mIoU benchmarking across both. Star the repo if you find these models useful! --- ## 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) --- ## Citation Please cite the following: ```bibtex @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} } ```