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naruto_hand_seal_detection
eeshawn11/naruto_hand_seal_detection
naruto_hand_seal_detection is a object detection model from eeshawn11. Use it when you need objects located in an image. It is set up for ultralytics.
<div align="center" <img width="640" alt="eeshawn11/narutohandsealdetection" src="https://huggingface.co/eeshawn11/narutohandsealdetection/resolve/main/thumbnail.jpg" </div
All-time downloads
1.8K
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Repo size
94.5 MB
Likes
3
Public
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At a glance
- Task
- Object Detection
- Library
- ultralytics
- Model type
- v8
- Access
- Public
- Created
- Mar 9, 2023
- Updated
- Mar 10, 2023
- SHA
- 2eadcbf9
Model card
From the Hugging Face model README
<div align="center">
<img width="640" alt="eeshawn11/naruto_hand_seal_detection" src="https://huggingface.co/eeshawn11/naruto_hand_seal_detection/resolve/main/thumbnail.jpg">
</div>
Supported Labels
['bird', 'boar', 'dog', 'dragon', 'hare', 'horse', 'monkey', 'ox', 'ram', 'rat', 'snake', 'tiger']
How to use
- Install ultralyticsplus:
pip install ultralyticsplus==0.0.28
- Load model and perform prediction:
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('eeshawn11/naruto_hand_seal_detection')
# set model parameters
model.overrides['conf'] = 0.50 # NMS confidence threshold
model.overrides['iou'] = 0.70 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 10 # 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
- Mar 9, 2023
- Updated
- Mar 10, 2023