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---
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 # since mAP@0.5 is not available on hf.co/metrics
value: 0.979 # min: 0.0 - max: 1.0
name: mAP@0.5(box)
inference: false
---
# YOLOv5 for Layout Detection
## Dataset
Dataset available in [kaggle](https://www.kaggle.com/datasets/hermansugiharto/book-layout)
## Supported Labels
```python
["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
```python
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()
```