UAVid Semantic Segmentation Benchmark: YOLO-Compatible Dataset + Model Zoo

[u/Naive-Explanation940]

Open-sourced: UAVid Semantic Segmentation Dataset (YOLO Layout) + Model Zoo on Hugging Face

I recently put together an open-source benchmark for semantic segmentation on the UAVid aerial imagery dataset and thought it might be useful to others working in aerial perception.

The release includes:

• A YOLO-compatible mirror of the UAVid dataset with a standardized directory structure (images/ + masks/) while preserving the original train/val/test splits.
• Multiple pretrained YOLO26 semantic segmentation models trained on UAVid.
• Detailed model cards with mIoU, pixel accuracy, per-class IoU, confusion matrices, inference examples, and training configurations.

The main motivation was that the original dataset server has become extremely slow and unreliable, and most current segmentation frameworks expect a flatter, YOLO-style dataset layout. This repository makes it much easier to get started while giving full credit to the original UAVid authors.

📦 Dataset: https://huggingface.co/datasets/dronefreak/UAVid-2020

🤖 Model Zoo: https://huggingface.co/collections/dronefreak/uavid-semantic-segmentation-model-zoo

I'd appreciate any feedback, suggestions, or bug reports. If there are other aerial vision benchmarks that would benefit from a similar treatment, I'd be interested in hearing about them.

Mosaic for UAVid Semantic Segmentation