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Extra features for ultralytics/ultralytics.
pip install ultralyticsplus
ultralyticsplus --exp_dir runs/detect/train --hf_model_id HF_USERNAME/MODELNAME
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('HF_USERNAME/MODELNAME')
# 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, imgsz=640)
# parse results
result = results[0]
boxes = result.boxes.xyxy # x1, y1, x2, y2
scores = result.boxes.conf
categories = result.boxes.cls
scores = result.probs # for classification models
masks = result.masks # for segmentation models
# show results on image
render = render_result(model=model, image=image, result=result)
render.show()
FAQs
HuggingFace utilities for Ultralytics/YOLOv8.
We found that ultralyticsplus demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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