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add ultralytics model card
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metadata
tags:
  - ultralyticsplus
  - yolov8
  - ultralytics
  - yolo
  - vision
  - image-classification
  - pytorch
library_name: ultralytics
library_version: 8.0.43
inference: false
model-index:
  - name: uisikdag/weed_yolov8_balanced
    results:
      - task:
          type: image-classification
        metrics:
          - type: accuracy
            value: 0.9
            name: top1 accuracy
          - type: accuracy
            value: 1
            name: top5 accuracy
uisikdag/weed_yolov8_balanced

Supported Labels

['Black-grass', 'Charlock', 'Cleavers', 'Common Chickweed', 'Common wheat', 'Fat Hen', 'Loose Silky-bent', 'Maize', 'Scentless Mayweed', 'Shepherds Purse', 'Small-flowered Cranesbill', 'Sugar beet']

How to use

pip install ultralyticsplus==0.0.28 ultralytics==8.0.43
  • Load model and perform prediction:
from ultralyticsplus import YOLO, postprocess_classify_output

# load model
model = YOLO('uisikdag/weed_yolov8_balanced')

# set model parameters
model.overrides['conf'] = 0.25  # model confidence threshold

# 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].probs) # [0.1, 0.2, 0.3, 0.4]
processed_result = postprocess_classify_output(model, result=results[0])
print(processed_result) # {"cat": 0.4, "dog": 0.6}