--- tags: - ultralyticsplus - yolov8 - ultralytics - yolo - vision - image-classification - pytorch - awesome-yolov8-models library_name: ultralytics library_version: 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 ---
keremberke/yolov8m-chest-xray-classification
### Supported Labels ``` ['NORMAL', 'PNEUMONIA'] ``` ### How to use - Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus): ```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} ```