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pid_yolov8
DanielCerda/pid_yolov8
pid_yolov8 is a object detection model from DanielCerda. Use it when you need objects located in an image. It is set up for ultralytics.
<div align="center" <img width="640" alt="DanielCerda/pidyolov8" src="https://huggingface.co/DanielCerda/pidyolov8/resolve/main/thumbnail.jpg" </div
All-time downloads
570
Public
Repo size
52.8 MB
Likes
2
Public
Repo makeup
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.pt52.1 MB · 97%
At a glance
- Task
- Object Detection
- Library
- ultralytics
- Model type
- v8
- Access
- Public
- Created
- Jan 31, 2024
- Updated
- Jan 31, 2024
- SHA
- bc29f57d
Datasets
Model card
From the Hugging Face model README
<div align="center">
<img width="640" alt="DanielCerda/pid_yolov8" src="https://huggingface.co/DanielCerda/pid_yolov8/resolve/main/thumbnail.jpg">
</div>
Supported Labels
['ball-valve', 'butterfly-valve', 'centrifugal-pump', 'check-valve', 'gate-valve']
How to use
- Install ultralyticsplus:
pip install ultralyticsplus==0.0.29 ultralytics==8.0.239
- Load model and perform prediction:
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('DanielCerda/pid_yolov8')
# 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)
render = render_result(model=model, image=image, result=results[0])
render.show()
- Task
- Object Detection
- Library
- ultralytics
- Type
- v8
- Created
- Jan 31, 2024
- Updated
- Jan 31, 2024