keremberke_yolov8m-table-extraction

---

tags:

• ultralyticsplus
• yolov8
• ultralytics
• yolo
• vision
• object-detection
• pytorch
• awesome-yolov8-models library_name: ultralytics library_version: 8.0.21 inference: false datasets:
• keremberke/table-extraction model-index:
• name: keremberke/yolov8m-table-extraction results:
  • task: type: object-detection dataset: type: keremberke/table-extraction name: table-extraction split: validation metrics:
    • type: precision value: 0.95194 name: [email protected](box)

license: agpl-3.0

Supported Labels

['bordered', 'borderless']

How to use

• Install ultralyticsplus:

pip install ultralyticsplus==0.0.23 ultralytics==8.0.21

• Load model and perform prediction:

from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('keremberke/yolov8m-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()

More models available at: awesome-yolov8-models