Yolov8s Table Extraction

🚀 keremberke/yolov8s-table-extraction

This model is designed for table extraction in the field of object detection, leveraging the power of YOLOv8 and ultralytics. It provides high precision in identifying tables in various formats.

✨ Features

• Object Detection: Specialized in detecting tables with high precision.
• Supported Labels: Capable of identifying bordered and borderless tables.

📦 Installation

• Install ultralyticsplus:

pip install ultralyticsplus==0.0.23 ultralytics==8.0.21

💻 Usage Examples

Basic Usage

from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('keremberke/yolov8s-table-extraction')

# set model parameters
model.overrides['conf'] = 0.25  # NMS confidence threshold
model.overrides['iou'] = 0.45  # NMS IoU threshold
model.overrides['agnostic_nms'] = False  # NMS class-agnostic
model.overrides['max_det'] = 1000  # maximum number of detections per image

# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model.predict(image)

# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()

📚 Documentation

Supported Labels

['bordered', 'borderless']

Model Information

Model Performance

More models available at: awesome-yolov8-models