--- tags: - ultralyticsplus - yolov8 - ultralytics - yolo - vision - object-detection - pytorch - visdrone - uav library_name: ultralytics library_version: 8.0.43 inference: false model-index: - name: mshamrai/yolov8x-visdrone results: - task: type: object-detection metrics: - type: precision value: 0.47047 name: mAP@0.5(box) license: openrail ---
mshamrai/yolov8x-visdrone
### Supported Labels ``` ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor'] ``` ### How to use - Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus): ```bash pip install ultralyticsplus==0.0.28 ultralytics==8.0.43 ``` - Load model and perform prediction: ```python from ultralyticsplus import YOLO, render_result # load model model = YOLO('mshamrai/yolov8x-visdrone') # set model parameters model.overrides['conf'] = 0.25 # NMS confidence threshold model.overrides['iou'] = 0.45 # NMS IoU threshold model.overrides['agnostic_nms'] = False # NMS class-agnostic model.overrides['max_det'] = 1000 # maximum number of detections per image # 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].boxes) render = render_result(model=model, image=image, result=results[0]) render.show() ```