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['BOM_GEN', 'BOM_JUN', 'BOM_KID', 'BOM_SAC', 'BOM_THV', 'BOM_THX', 'BOM_VTG', 'BOM_YTV', 'HAN_GLI', 'HOP_FEJ', 'HOP_FRE', 'HOP_JUN', 'HOP_POC', 'HOP_VTG', 'HOP_YTV', 'LOC_JUN', 'LOC_KID', 'LOC_YTV', 'LOO_DAU', 'LOO_KID', 'LOO_MAM', 'LOO_YTV', 'POS_GLI', 'POS_LON', 'POS_NHO', 'POS_THA', 'TUI_GEN', 'TUI_JUN', 'TUI_KID', 'TUI_SAC', 'TUI_THV', 'TUI_THX', 'TUI_VTG', 'TUI_YTV']pip install ultralyticsplus==0.0.29 ultralytics==8.0.2391from ultralyticsplus import YOLO, render_result
2
3# load model
4model = YOLO('chanelcolgate/chamdiemgianhang-vsk-v9')
5
6# set model parameters
7model.overrides['conf'] = 0.25 # NMS confidence threshold
8model.overrides['iou'] = 0.45 # NMS IoU threshold
9model.overrides['agnostic_nms'] = False # NMS class-agnostic
10model.overrides['max_det'] = 1000 # maximum number of detections per image
11
12# set image
13image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
14
15# perform inference
16results = model.predict(image)
17
18# observe results
19print(results[0].boxes)
20render = render_result(model=model, image=image, result=results[0])
21render.show()