--- tags: - ultralyticsplus - yolov8 - ultralytics - yolo - vision - object-detection - pytorch library_name: ultralytics library_version: 8.0.43 inference: false model-index: - name: linhcuem/checker_TB_yolov8_ver3 results: - task: type: object-detection metrics: - type: precision # since mAP@0.5 is not available on hf.co/metrics value: 0.94985 # min: 0.0 - max: 1.0 name: mAP@0.5(box) ---
linhcuem/checker_TB_yolov8_ver3
### Supported Labels ``` ['bom_gen', 'bom_jn', 'bom_knp', 'bom_sachet', 'bom_vtgk', 'bom_ytv', 'hop_dln', 'hop_jn', 'hop_vtg', 'hop_ytv', 'lo_kids', 'lo_ytv', 'loc_dln', 'loc_jn', 'loc_kids', 'loc_ytv', 'pocky', 'tui_gen', 'tui_jn', 'tui_sachet', 'tui_vtgk'] ``` ### 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('linhcuem/checker_TB_yolov8_ver3') # 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() ```