detr-resnet-50-finetuned-construction-safety

<div align="center"> <img width="640" alt="keremberke/yolov8m-protective-equipment-detection" src="https://huggingface.co/keremberke/yolov8m-protective-equipment-detection/resolve/main/thumbnail.jpg"> </div>

Supported Labels

['glove', 'goggles', 'helmet', 'mask', 'no_glove', 'no_goggles', 'no_helmet', 'no_mask', 'no_shoes', 'shoes']

How to use

• Install ultralyticsplus:

pip install ultralyticsplus==0.0.24 ultralytics==8.0.23

• Load model and perform prediction:

from ultralyticsplus import YOLO, render_result

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

# 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