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uisikdag/yolo-v8-plant-seedling-classification

sourceHugging Faceupdated 3y agoView on Hugging Face
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tags:

  • —ultralyticsplus
  • —yolov8
  • —ultralytics
  • —yolo
  • —vision
  • —image-classification
  • —pytorch

libraryname: ultralytics libraryversion: 8.0.43 inference: false

model-index:

  • —name: uisikdag/weedyolov8balanced results:
  • —task: type: image-classification

metrics:

  • —type: accuracy value: 0.9 # min: 0.0 - max: 1.0 name: top1 accuracy
  • —type: accuracy value: 1.0 # min: 0.0 - max: 1.0 name: top5 accuracy ---

<div align="center"> <img width="640" alt="uisikdag/weedyolov8balanced" src="https://huggingface.co/uisikdag/weedyolov8balanced/resolve/main/thumbnail.jpg"> </div>

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

bash
pip install ultralyticsplus==0.0.28 ultralytics==8.0.43
  • —Load model and perform prediction:
python
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}