README.md

metadata

tags:
  - ultralyticsplus
  - yolov5
  - ultralytics
  - yolo
  - vision
  - object-detection
  - pytorch
  - awesome-yolov8-models
  - indonesia
  - layout detector
model-index:
  - name: hermanshid/yolo-layout-detector
    results:
      - task:
          type: object-detection
        metrics:
          - type: precision
            value: 0.979
            name: mAP@0.5(box)
inference: false

YOLOv5 for Layout Detection

Dataset

Dataset available in kaggle

Supported Labels

["caption", "chart", "image", "image_caption", "table", "table_caption", "text", "title"]

How to use

• Install library

pip install yolov5==7.0.5 torch

Load model and perform prediction

import yolov5
from PIL import Image

model = yolov5.load(models_id)

model.overrides['conf'] = 0.25  # NMS confidence threshold
model.overrides['iou'] = 0.45  # NMS IoU threshold
model.overrides['max_det'] = 1000  # maximum number of detections per image

# set image
image = 'https://huggingface.co/spaces/hermanshid/yolo-layout-detector-space/raw/main/test_images/example1.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()