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...
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 21,5 MB
- Versión
- vRevision 15be268
- Actualizado
- 1 nov 2026
- Descargas
- 87
- Fuente
- Hugging Face
Descripción de la fuente
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
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 1 nov
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- 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...
Source context: 87 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo yahyagul/traffic-sign-yolov8
Source context: 40 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo yahyagul/traffic-sign-yolov8
