keremberke/yolov8m-pcb-defect-segmentation

tags:

• —ultralyticsplus
• —yolov8
• —ultralytics
• —yolo
• —vision
• —image-segmentation
• —pytorch
• —awesome-yolov8-models libraryname: ultralytics libraryversion: 8.0.23 inference: false

datasets:

• —keremberke/pcb-defect-segmentation

model-index:

• —name: keremberke/yolov8m-pcb-defect-segmentation results:
• —task: type: image-segmentation

dataset: type: keremberke/pcb-defect-segmentation name: pcb-defect-segmentation split: validation

metrics:

• —type: precision # since mAP@0.5 is not available on hf.co/metrics value: 0.56836 # min: 0.0 - max: 1.0 name: mAP@0.5(box)
• —type: precision # since mAP@0.5 is not available on hf.co/metrics value: 0.5573 # min: 0.0 - max: 1.0 name: mAP@0.5(mask) ---

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

Supported Labels

['Dry_joint', 'Incorrect_installation', 'PCB_damage', 'Short_circuit']

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-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](https://yolov8.xyz)