---
license: agpl-3.0
pipeline_tag: image-segmentation
library_name: ultralytics
base_model: "Ultralytics/YOLO26"
tags:
- semantic-segmentation
- aerial-imagery
- drone
- aeroscapes
- yolo26
- ultralytics
- pytorch
- computer-vision
datasets:
- dronefreak/Aeroscapes
metrics:
- miou
- pixel-accuracy
---
# YOLO26s-sem Finetuned on AeroScapes
     
Fine-tuned YOLO26s semantic segmentation model for aerial drone imagery using the AeroScapes benchmark dataset.
This model is part of the **AeroScapes Semantic Segmentation Model Zoo**, a collection of CABiNet and YOLO26 models trained and evaluated under a common pipeline for aerial semantic segmentation.
Qualitative results on AeroScapes val-split examples — single-scale (imgsz=896) inference, no TTA.
---
## Performance
| Metric | Score |
| ------------------- | --------------- |
| mIoU | 65.44 |
| Pixel Accuracy | 93.22 |
| Parameters (M) | 6.50 |
| FLOPs (GFLOPs @ 720px) | 21.9 |
---
## AeroScapes Model Zoo
| Rank | Model | mIoU (%) | Pixel Acc (%) | Params (M) | FLOPs (GFLOPs) |
| ---- | --------------------- | ------------- | ------------------ | ----------------- | ----------------- |
| 1 | YOLO26x-sem | 68.36 | 93.91 | 40.15 | 213.0 |
| 2 | YOLO26l-sem | 68.0 | 93.16 | 17.86 | 95.1 |
| 3 | CABiNet (MobileNetV3-Large) | 67.83 | 94.32 | 9.18 | 27.4 |
| 4 | YOLO26m-sem | 66.97 | 93.51 | 14.31 | 75.2 |
| 5 | YOLO26s-sem | 65.44 | 93.22 | 6.50 | 21.9 |
| 6 | YOLO26n-sem | 64.86 | 92.84 | 1.63 | 5.6 |
---
## Per-Class IoU (%)
| Class | YOLO26x-sem | YOLO26l-sem | CABiNet (MobileNetV3-Large) | YOLO26m-sem | YOLO26s-sem | YOLO26n-sem |
| --- | --- | --- | --- | --- | --- | --- |
| Background | 81.69 | 79.43 | 82.67 | 80.75 | 79.31 | 78.43 |
| Person | 53.34 | 49.28 | 46.9 | 50.81 | 49.44 | 44.51 |
| Bike | 35.61 | 42.18 | 42.35 | 38.4 | 30.09 | 35.7 |
| Car | 86.16 | 87.67 | 85.08 | 84.76 | 84.54 | 85.28 |
| Drone | 60.46 | 65.97 | 61.77 | 58.91 | 59.73 | 50.88 |
| Boat | 82.04 | 79.76 | 77.67 | 80.33 | 76.19 | 80.28 |
| Animal | 41.21 | 37.08 | 33.74 | 38.22 | 36.21 | 37.15 |
| Obstacle | 26.91 | 29.17 | 23.13 | 24.14 | 19.8 | 17.29 |
| Construction | 75.76 | 67.09 | 78.81 | 72.87 | 71.02 | 74.39 |
| Vegetation | 94.07 | 93.6 | 94.3 | 93.99 | 93.93 | 93.55 |
| Road | 92.14 | 92.07 | 92.91 | 91.94 | 91.64 | 89.1 |
| Sky | 90.96 | 92.72 | 94.57 | 88.57 | 93.35 | 91.73 |
---
## Evaluation Visualizations
### Per-Class IoU Bar Chart
### Confusion Matrix
### Loss Curves
---
## Dataset
[AeroScapes](https://github.com/ishann/aeroscapes) is an aerial semantic segmentation benchmark of ~3,200 720p images captured by a fleet of drones across roughly 10 flight sequences, covering urban and suburban scenes with people, vehicles, and small objects.
### Classes
- Background
- Person
- Bike
- Car
- Drone
- Boat
- Animal
- Obstacle
- Construction
- Vegetation
- Road
- Sky
---
## 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/aeroscapes-yolo26s-sem",
filename="best.pt"
)
model = YOLO(weights)
```
### Run Inference
```python
results = model.predict(source="image.png", task="semantic", imgsz=896)
mask = results[0].semantic_mask.cpu().numpy().data # (H, W) class-ID map
```
---
## Training Configuration
| Setting | Value |
| ------------ | ------------------------------------------ |
| Epochs | 120 |
| Image size | 896 |
| Batch size | 8 |
| Dataset | AeroScapes (converted images/+masks/ format) |
| Framework | Ultralytics YOLO |
| cls_pw (class weighting) | 0.5 |
---
## Official Resources
- **AeroScapes Semantic Segmentation Model Zoo:** https://huggingface.co/collections/dronefreak/aeroscapes-semantic-segmentation-model-zoo
- **CABiNet repository:** https://github.com/dronefreak/CABiNet
- **CABiNet Paper:** https://arxiv.org/abs/2011.00993v2
- **AeroScapes Dataset (Hugging Face):** https://huggingface.co/datasets/dronefreak/Aeroscapes
- **AeroScapes Repository:** https://github.com/ishann/aeroscapes
- **AeroScapes Paper (CMU PDF):** https://www.cs.cmu.edu/~deva/papers/aeroscapes.pdf
- **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 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:
* Very rare classes (Bike, Drone, Boat) each cover well under 2% of any single frame in the val split — expect unreliable predictions for these
* No official test split — all reported numbers are on the val split, unlike UAVid/VDD
* Frames are drawn from only ~10 flight sequences, so scene diversity is lower than UAVid/VDD despite the image count
* Native resolution (1280x720) downsampled to the eval imgsz — fine detail on small objects (pedestrians, bikes) can be lost
---
## Citation
Please cite the following:
```bibtex
@inproceedings{nigam2018ensemble,
title={Ensemble Knowledge Transfer for Semantic Segmentation},
author={Nigam, Ishan and Huang, Chen and Ramanan, Deva},
booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
year={2018}
}
@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}
}
```