metadata
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- image-segmentation
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/pcb-defect-segmentation
model-index:
- name: keremberke/yolov8n-pcb-defect-segmentation
results:
- task:
type: image-segmentation
dataset:
type: keremberke/pcb-defect-segmentation
name: pcb-defect-segmentation
split: validation
metrics:
- type: precision
value: 0.51186
name: [email protected](box)
- type: precision
value: 0.51667
name: [email protected](mask)
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