---
license: agpl-3.0
library_name: ultralytics
pipeline_tag: object-detection
tags:
  - yolo
  - yolo26
  - traffic
  - vehicles
  - dashcam
  - south-asia
  - autonomous-driving
datasets:
  - visaitech/vehicle-mixed-traffic-detection
---

# 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)](https://huggingface.co/datasets/visaitech/vehicle-mixed-traffic-detection).
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

![Risk-zone demo on a congested street](demo.gif)

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](https://github.com/visaitech/mixed-traffic-risk-detector).

## Usage

```python
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}
}
```