metadata
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- image-segmentation
- pytorch
library_name: ultralytics
library_version: 8.0.6
inference: false
model-index:
- name: fcakyon/test-model
results:
- task:
type: image-segmentation
metrics:
- type: precision
value: 0.63311
name: mAP@0.5(box)
- type: precision
value: 0.60214
name: mAP@0.5(mask)
Supported Labels
['Cracks-and-spalling', 'object']
How to use
- Install ultralytics and ultralyticsplus:
pip install -U ultralytics ultralyticsplus
- Load model and perform prediction:
from ultralyticsplus import YOLO, render_model_output
# load model
model = YOLO('fcakyon/test-model')
# 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
for result in model.predict(image, return_outputs=True):
print(result["det"]) # [[x1, y1, x2, y2, conf, class]]
print(result["segment"]) # [segmentation mask]
render = render_model_output(model=model, image=image, model_output=result)
render.show()