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traffic-sign-yolov8
yahyagul/traffic-sign-yolov8
traffic-sign-yolov8 is a object detection model from yahyagul. Use it when you need objects located in an image. It is set up for ultralytics. The card lists the license as mit.
A YOLOv8 object detection model fine-tuned via transfer learning for real-time traffic sign detection and classification. Trained as part of a BS Software Engineering thesis project and subsequently deployed as a publ…
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At a glance
- Task
- Object Detection
- Library
- ultralytics
- License
- mit
- Access
- Public
- Created
- Jul 1, 2026
- Updated
- Jul 1, 2026
- SHA
- 15be2681
Datasets
Model card
From the Hugging Face model README
Traffic Sign Detection & Recognition — YOLOv8
A YOLOv8 object detection model fine-tuned via transfer learning for real-time traffic sign detection and classification. Trained as part of a BS Software Engineering thesis project and subsequently deployed as a public web application.
Live Demo
🚦 Try it on Hugging Face Spaces — supports image upload, video processing, and live webcam capture.
Model Details
| Property | Value |
|---|---|
| Base model | YOLOv8 (Ultralytics) |
| Training method | Transfer learning |
| Task | Object detection |
| Input | RGB image or video frame |
| Output | Bounding boxes + class labels + confidence scores |
Classes (21 traffic sign categories)
| ID | Label | ID | Label |
|---|---|---|---|
| 0 | bus_stop | 11 | ped_crossing |
| 1 | do_not_enter | 12 | ped_zebra_cross |
| 2 | do_not_stop | 13 | railway_crossing |
| 3 | do_not_turn_l | 14 | red_light |
| 4 | do_not_turn_r | 15 | stop |
| 5 | do_not_u_turn | 16 | t_intersection_l |
| 6 | enter_left_lane | 17 | traffic_light |
| 7 | green_light | 18 | u_turn |
| 8 | left_right_lane | 19 | warning |
| 9 | no_parking | 20 | yellow_light |
| 10 | parking |
Usage
from ultralytics import YOLO
import torch
# Allow loading of custom YOLOv8 checkpoint
_orig = torch.load
def _safe_load(*args, **kwargs):
kwargs["weights_only"] = False
return _orig(*args, **kwargs)
torch.load = _safe_load
# Load model
model = YOLO("best_roboflow.pt")
# Run inference
results = model.predict("your_image.jpg", conf=0.4)
results[0].show()
Use Cases
- Driver-assistance systems (ADAS): real-time alerting for speed limits, stop signs, and warning signs
- Dashcam footage auditing: automatically flagging missed or obscured signage for road-safety compliance reviews
- Autonomous vehicle perception: as a building-block detection module feeding into higher-level driving decision systems
Training Details
- Dataset sourced and annotated via Roboflow
- Fine-tuned from YOLOv8 pretrained weights via transfer learning
- 21 traffic sign classes covering common urban road signage
Deployment
This model is deployed as a Streamlit web application on Hugging Face Spaces. See the traffic-sign-detector Space for the full application code and deployment details.
Author
- Task
- Object Detection
- Library
- ultralytics
- License
- mit
- Created
- Jul 1, 2026
- Updated
- Jul 1, 2026