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
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
- Install ultralyticsplus:
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}