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README.md

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+---
+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)
+
+## 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}
+}
+```