visaitech/vehicle-mixed-traffic-yolo26n

Mixed-Traffic Vehicle Detector, YOLO26n (v0.1)

A YOLO26n fine-tune that detects pedestrian / 2-wheeler / 3-wheeler / 4-wheeler in South Asian mixed traffic, trained on the Visaitech Mixed-Traffic Vehicle Detection dataset (v0.1). The class taxonomy covers what general-purpose COCO detectors don't: an auto-rickshaw class, and a single 2-wheeler class for how motorcycles and bicycles actually mix in this traffic.

This is an early v0.1 baseline trained on 239 images: a working proof of concept, not a production-grade detector. Expect it to improve as the dataset grows.

• Architecture: YOLO26n (nano), ~2.5M parameters, ~5.4MB weights
• Input size: 640x640
• Classes: pedestrian (0), 2-wheeler (1), 3-wheeler (2), 4-wheeler (3)

Demo

The model wired into a simple proximity risk overlay: safe objects get a thin green outline, close objects are tagged orange (caution) or red (critical). The footage is a clip from the raw archive that ships with the training dataset. A higher-quality video of the same run is in this repo as demo.mp4, and the overlay code plus a runnable Gradio app live at github.com/visaitech/mixed-traffic-risk-detector.

Usage

from ultralytics import YOLO

model = YOLO("best.pt")
results = model.predict("street.jpg")
results[0].show()

Evaluation

Validation split: 54 images / 209 boxes, held out by whole source video (not by frame) so near-duplicate frames never straddle the train/val boundary.

The model was trained and evaluated on the original frames. The published dataset is the anonymized copy (pixelated heads and license plates, blacked-out timestamp region), which changes object appearance enough to lower the numbers, so both are reported. If you re-run validation against the public dataset, expect the second table.

On original (non-public) frames

class	instances	precision	recall	mAP50	mAP50-95
pedestrian	9	0.688	0.333	0.383	0.250
2-wheeler	128	0.653	0.875	0.796	0.502
3-wheeler	32	0.494	0.854	0.764	0.560
4-wheeler	40	0.915	0.875	0.934	0.704
all	209	0.688	0.734	0.719	0.504

On the published anonymized frames

class	instances	precision	recall	mAP50	mAP50-95
pedestrian	9	0.656	0.333	0.251	0.166
2-wheeler	128	0.620	0.750	0.695	0.332
3-wheeler	32	0.538	0.719	0.708	0.509
4-wheeler	40	0.485	0.875	0.862	0.648
all	209	0.575	0.669	0.629	0.414

Training details

• Fine-tuned from the official yolo26n.pt COCO checkpoint with Ultralytics defaults (auto optimizer, lr0 0.01, seed 0), image size 640, batch 8.
• Early-stopped at epoch 105 of a 150-epoch budget (patience 30); best checkpoint from epoch 75.
• Trained on CPU (laptop-class i7-10510U, ~94s/epoch, under 3 hours total): the dataset is small enough that this was more stable than the available 2GB-VRAM laptop GPU.

Limitations

• Weak on pedestrians: recall is only 0.333. The v0.1 training set has just 39 pedestrian boxes (~2.6% of all boxes), so this class needs more data, not more training. Do not rely on this model to find people.
• Daytime only: all training footage is daylight (10:14 to 16:19). Performance on night footage is untested and likely poor.
• Single camera setup: all frames come from the same dashcam hardware and region. Expect domain shift on other cameras, mounting positions, or countries.
• Not a safety component: this is a portfolio/research baseline. Do not use it for collision avoidance, driver assistance, or any safety-critical decision.

License

AGPL-3.0. The model is a fine-tune of Ultralytics YOLO26n weights and is used via the AGPL-3.0-licensed ultralytics package, so the weights are released under the same license. The training dataset is separately licensed CC BY 4.0.

Citation

@misc{visaitech_mixed_traffic_yolo26n_2026,
  title        = {Mixed-Traffic Vehicle Detector, YOLO26n},
  author       = {Visaitech},
  year         = {2026},
  version      = {0.1},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/visaitech/vehicle-mixed-traffic-yolo26n}
}