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keremberke/yolov8m-chest-xray-classification

sourceHugging Faceupdated 4y agoView on Hugging Face
5likes982downloads
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tags:

  • —ultralyticsplus
  • —yolov8
  • —ultralytics
  • —yolo
  • —vision
  • —image-classification
  • —pytorch
  • —awesome-yolov8-models libraryname: ultralytics libraryversion: 8.0.23 inference: false

datasets:

  • —keremberke/chest-xray-classification

model-index:

  • —name: keremberke/yolov8m-chest-xray-classification results:
  • —task: type: image-classification

dataset: type: keremberke/chest-xray-classification name: chest-xray-classification split: validation

metrics:

  • —type: accuracy value: 0.95533 # min: 0.0 - max: 1.0 name: top1 accuracy
  • —type: accuracy value: 1 # min: 0.0 - max: 1.0 name: top5 accuracy ---

<div align="center"> <img width="640" alt="keremberke/yolov8m-chest-xray-classification" src="https://huggingface.co/keremberke/yolov8m-chest-xray-classification/resolve/main/thumbnail.jpg"> </div>

Supported Labels

['NORMAL', 'PNEUMONIA']

How to use

bash
pip install ultralyticsplus==0.0.24 ultralytics==8.0.23
  • —Load model and perform prediction:
python
from ultralyticsplus import YOLO, postprocess_classify_output

# load model
model = YOLO('keremberke/yolov8m-chest-xray-classification')

# 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}

More models available at: [awesome-yolov8-models](https://yolov8.xyz)