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yolov8l-visdrone
mshamrai/yolov8l-visdrone
yolov8l-visdrone is a object detection model from mshamrai. Use it when you need objects located in an image. It is set up for ultralytics. The card lists the license as openrail.
<div align="center" <img width="640" alt="mshamrai/yolov8l-visdrone" src="https://huggingface.co/mshamrai/yolov8l-visdrone/resolve/main/thumbnail.jpg" </div
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Repo size
92.3 MB
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5
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At a glance
- Task
- Object Detection
- Library
- ultralytics
- License
- openrail
- Model type
- v8
- Access
- Public
- Created
- May 29, 2023
- Updated
- Mar 24, 2025
- SHA
- 4edd684c
Model card
From the Hugging Face model README
<div align="center">
<img width="640" alt="mshamrai/yolov8l-visdrone" src="https://huggingface.co/mshamrai/yolov8l-visdrone/resolve/main/thumbnail.jpg">
</div>
Supported Labels
['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']
How to use
- Install ultralyticsplus:
pip install ultralyticsplus==0.0.28 ultralytics==8.0.43
- Load model and perform prediction:
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('mshamrai/yolov8l-visdrone')
# 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
- License
- openrail
- Created
- May 29, 2023
- Updated
- Mar 24, 2025