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Madan11/
two-wheeler-detector

Name: two-wheeler-detector
Availability: InStock

two-wheeler-detection

agpl-3.0

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Two-Wheeler & Vehicle Detector (YOLOv8n)

A custom-trained YOLOv8n object detection model for detecting vehicles in images and video, with a focus on two-wheelers (motorcycles, scooters, bicycles) alongside other common vehicle types.

Model Details

Architecture: YOLOv8n (Ultralytics)
Task: Object Detection
Base weights: yolov8n.pt (fine-tuned)
Training epochs: 25
Image size: 640x640
Batch size: 16

Classes

ID	Class
0	auto_rickshaw
1	bicycle
2	bus
3	car
4	motorcycle
5	pickup
6	scooter
7	truck
8	van

Performance (final epoch)

Metric	Value
Precision	0.865
Recall	0.695
mAP50	0.752
mAP50-95	0.624

See confusion_matrix.png and results.png in this repo for detailed training curves.

Usage

pip install ultralytics

python

from ultralytics import YOLO

# Load model directly from the Hub
model = YOLO("https://huggingface.co/Madan11/two-wheeler-detector/resolve/main/best.pt")

results = model.predict("path/to/image.jpg", conf=0.25, save=True)

Or download the weights first with huggingface_hub:

python

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

model_path = hf_hub_download(repo_id="Madan11/two-wheeler-detector", filename="best.pt")
model = YOLO(model_path)
results = model.predict("path/to/image.jpg", conf=0.25, save=True)

Intended Use

Detecting and classifying vehicles (with emphasis on two-wheelers) in images, video, or webcam streams — e.g. traffic monitoring, parking analysis, or road-safety applications.

Limitations

Trained on a custom dataset; performance may vary on data with different lighting, camera angles, or geographic/vehicle-style distributions not represented in training.
Recall (0.695) is lower than precision, meaning the model is more likely to miss some vehicles than to falsely detect non-vehicles — tune the confidence threshold as needed for your use case.