Yolov8m Table Extraction

🚀 keremberke/yolov8m-table-extraction

本項目基於ultralyticsplus和YOLOv8，用於表格提取的目標檢測任務。它能準確識別有邊框和無邊框表格，在keremberke/table-extraction數據集上表現出色。

🚀 快速開始

支持的標籤

['bordered', 'borderless']

如何使用

• 安裝 ultralyticsplus：

pip install ultralyticsplus==0.0.23 ultralytics==8.0.21

• 加載模型並進行預測：

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()

更多模型請訪問：awesome-yolov8-models

📦 安裝指南

安裝所需的庫，使用以下命令：

pip install ultralyticsplus==0.0.23 ultralytics==8.0.21

💻 使用示例

基礎用法

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()

📚 詳細文檔

模型信息

評估指標

在keremberke/table-extraction數據集的驗證集上，模型的mAP@0.5(box)精度為 0.95194。

📄 許可證

本項目採用AGPL-3.0許可證。