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Traffic Sign 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...

Sourcehuggingface.co/yahyagul/traffic-sign-yolov8/tree/15be2681d3b6e666f45c744219aab84e97a2f1b4
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Kind
Other
File size
21.5 MB
Version
vRevision 15be268
Updated
Nov 1, 2026
Downloads
87
Source
Hugging Face

Source description

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

PropertyValue
Base modelYOLOv8 (Ultralytics)
Training methodTransfer learning
TaskObject detection
InputRGB image or video frame
OutputBounding boxes + class labels + confidence scores

Classes (21 traffic sign categories)

IDLabelIDLabel
0bus_stop11ped_crossing
1do_not_enter12ped_zebra_cross
2do_not_stop13railway_crossing
3do_not_turn_l14red_light
4do_not_turn_r15stop
5do_not_u_turn16t_intersection_l
6enter_left_lane17traffic_light
7green_light18u_turn
8left_right_lane19warning
9no_parking20yellow_light
10parking

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

Mian Yahya Gul LinkedIn · GitHub

Sources

1 source
Hugging FacePrimaryVersion 15be2681d3b6e666f45c744219aab84e97a2f1b4
huggingface.co/yahyagul/traffic-sign-yolov8/tree/15be2681d3b6e666f45c744219aab84e97a2f1b4

Verified Nov 1

Model artifacts

1 artifact

best_roboflow.pt

pt · 21.5 MB · SHA-256 93793cc99ffd…6e2b · Hugging Face

Download file

Source excerpts

3 excerpts
ReadmeAug 18huggingface.co/yahyagul/traffic-sign-yolov8

--- license: mit tags: - object-detection - computer-vision - yolov8 - traffic-signs - ultralytics - autonomous-driving datasets: - roboflow pipeline_tag: object-detection --- # 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](https://huggingface.co/spaces/yahyagul/traffic-sign-detector) — 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 ```python 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 deplo...

NoteNov 1huggingface.co/yahyagul/traffic-sign-yolov8

Source context: 87 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo yahyagul/traffic-sign-yolov8

NoteAug 18huggingface.co/yahyagul/traffic-sign-yolov8

Source context: 40 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo yahyagul/traffic-sign-yolov8