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ultralyticsplus/yolov8s

sourceHugging Faceagpl-3.0updated 3y agoView on Hugging Face
46likes623downloads
Model Card

license: agpl-3.0 tags:

  • —ultralyticsplus
  • —ultralytics
  • —yolov8
  • —yolo
  • —vision
  • —object-detection
  • —pytorch libraryname: ultralytics libraryversion: 8.0.4 inference: false

model-index:

  • —name: ultralyticsplus/yolov8s results:
  • —task: type: object-detection

metrics:

  • —type: precision # since mAP is not available on hf.co/metrics value: 0.449 # min: 0.0 - max: 1.0 name: mAP ---

Supported Labels

['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']

How to use

bash
pip install -U ultralyticsplus==0.0.14
  • —Load model and perform prediction:
python
from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('ultralyticsplus/yolov8s')

# 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()