Object Detection
ultralytics
PyTorch
yolosv5
ultralyticsplus
yolov5
yolo
vision
awesome-yolov8-models
indonesia
layout detector
Eval Results (legacy)
Instructions to use hermanshid/yolo-layout-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use hermanshid/yolo-layout-detector with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("hermanshid/yolo-layout-detector", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from hermanshid/yolo-layout-detector: direct link, hf CLI and curl.
- Browser
- Download file 1.37 kB
-
https://huggingface.co/hermanshid/yolo-layout-detector/resolve/main/README.md
- Command line
-
hf download hf://hermanshid/yolo-layout-detector/README.md
-
curl -L -o README.md https://huggingface.co/hermanshid/yolo-layout-detector/resolve/main/README.md
1.37 kB
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()