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betty0/uav-traffic-vision

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uav-traffic-vision: YOLO26 on VisDrone2019-DET

Drone-view object detection trained on VisDrone2019-DET (10 classes: pedestrian, people, bicycle, car, van, truck, tricycle, awning-tricycle, bus, motor). Part of a portfolio project also covering SAHI sliced inference, ByteTrack-based traffic flow counting, and edge deployment benchmarks — full code and writeups: GitHub repo. Live demo: HF Space.

Files

file	description
`yolo26s_visdrone_640.pt`	yolo26s, imgsz=640, 97 epochs (early-stopped)
`yolo26s_visdrone_1024.pt`	yolo26s, imgsz=1024, 74 epochs (early-stopped, best at epoch 54) — higher accuracy at the same inference latency on a desktop GPU (see benchmarks)
`yolo26s_visdrone_640.onnx`	ONNX export of the 640 model, end-to-end (NMS-free), used by the Gradio demo

Evaluation (VisDrone2019-DET val, 548 images)

Same evaluation protocol across all rows (pycocotools, conf=0.01, custom tiny/small/medium/large area buckets — see the GitHub repo for the exact methodology).

setting	AP50	AP@[.5:.95]	AP tiny (<16px)	AP small	AP medium	AP large
yolo26s @ 640, direct	0.380	0.222	0.075	0.184	0.316	0.480
yolo26s @ 1024, direct	0.480	0.291	0.119	0.261	0.403	0.463
yolo26s @ 640 + SAHI 512/0.2	0.465	0.269	0.134	0.248	0.349	0.455

No single setting dominates every object-size bucket: SAHI wins on the smallest objects (native-resolution tiling), the 1024 checkpoint wins small/medium objects with no slicing overhead, and plain 640 direct is marginally best on large objects. See the GitHub repo's README for the full discussion.

Edge deployment (measured on a desktop RTX 4090 / host CPU — not a Jetson)

backend	mean latency	FPS
PyTorch .pt (RTX 4090)	13.4 ms	74.6
ONNX (CPU)	49.0 ms	20.4
TensorRT FP16 (RTX 4090)	11.5 ms	86.8

YOLO26 exports end-to-end (NMS-free) by default: the ONNX/TensorRT graph output is a fixed (1, 300, 6) tensor with no NMS op, which simplifies onboard deployment (no NMS-plugin version dependency, latency independent of scene density). TensorRT engines are architecture-specific and are not included here — rebuild on your target GPU with model.export(format="engine").

Usage

python

from ultralytics import YOLO

model = YOLO("yolo26s_visdrone_1024.pt")  # or _640.pt / .onnx
results = model.predict("your_drone_image.jpg", imgsz=1024)
results[0].show()

Intended use & limitations

—Trained for aerial/drone-viewpoint object detection at the altitudes and camera angles represented in VisDrone (urban streets, intersections, campuses). Not validated for other viewpoints (ground-level, satellite).
—Small-object detection remains the hardest case (see the tiny-object AP figures above) — for safety- or compliance-critical use, pair with SAHI sliced inference or the 1024 checkpoint rather than the 640 direct baseline alone.
—Heavy class imbalance in the source data (144.9k car instances vs 3.2k awning-tricycle in training) — expect weaker recall on the rarer vehicle classes.

Dataset license — please read

VisDrone2019 (AISKYEYE team, Tianjin University) is released for academic / research use only. These weights were trained on VisDrone and inherit that restriction — this is not a general-purpose commercially-licensed model. The dataset itself is not redistributed in this repository.