keremberke
keremberke/yolov8m-pcb-defect-segmentation
- ultralyticsplus - yolov8 - ultralytics - yolo - vision - image-segmentation - pytorch - awesome-yolov8-models libraryname: ultralytics lib...
Model Documentation
Supported Labels
['Dry_joint', 'Incorrect_installation', 'PCB_damage', 'Short_circuit']
How to use
bash
pip install ultralyticsplus==0.0.24 ultralytics==8.0.23
python
from ultralyticsplus import YOLO, render_result
load model
model = YOLO('keremberke/yolov8m-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
Files & Weights
| Filename | Size | Action |
|---|---|---|
| best.pt | 0.05 GB |