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Sponsored by
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keremberke / yolov8n-pcb-defect-segmentation
Model's Last Updated: February 22 2023
image-segmentation
Introduction of yolov8n-pcb-defect-segmentation
Model Details of yolov8n-pcb-defect-segmentation
Supported Labels
['Dry_joint', 'Incorrect_installation', 'PCB_damage', 'Short_circuit']
How to use
- Install ultralyticsplus :
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
- Load model and perform prediction:
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('keremberke/yolov8n-pcb-defect-segmentation')
# 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)
print(results[0].masks)
render = render_result(model=model, image=image, result=results[0])
render.show()
More models available at: awesome-yolov8-models
Runs of keremberke yolov8n-pcb-defect-segmentation on huggingface.co
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More Information About yolov8n-pcb-defect-segmentation huggingface.co Model
Url of yolov8n-pcb-defect-segmentation
yolov8n-pcb-defect-segmentation huggingface.co Url
Provider of yolov8n-pcb-defect-segmentation huggingface.co
Other API from keremberke
Updated:May 23 2024
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