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yye/uavid-aerial-segmentation

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App README

UAVid Semantic Segmentation — CABiNet & YOLO26 Model Zoo

Interactive demo of the **UAVid Semantic Segmentation Model Zoo** — pick any model from the dropdown and run it on an oblique aerial / drone urban scene, trained on the UAVid benchmark:

Model	mIoU (%)	Params (M)	FLOPs (GFLOPs)	HF Weights
CABiNet (MobileNetV3-Large)	68.60	9.17	54.8	link
CABiNet (MobileNetV3-Small)	66.84	5.36	44.1	link
YOLO26x-sem	64.41	40.16	430.9	link
YOLO26l-sem	63.28	17.87	192.4	link
YOLO26m-sem	61.98	14.32	152.3	link
YOLO26s-sem	61.69	6.50	44.4	link
YOLO26n-sem	58.17	1.63	11.4	link

CABiNet (MobileNetV3-Large) is the top performer — it beats every YOLO26 variant, including the largest (YOLO26x), on mIoU while using a fraction of the compute.

Every pixel is classified into one of 8 classes: Clutter, Building, Road, Static Car, Tree, Vegetation, Human, Moving Car. The demo renders a colored overlay and a per-class legend for whichever model you select.

Example images are real UAVid validation frames from `dronefreak/UAVid-2020`.

Runs on ZeroGPU.