Yolov8m Hard Hat Detection

--- tags: - ultralyticsplus - yolov8 - ultralytics - yolo - vision - object-detection - pytorch - awesome-yolov8-models library_name: ultralytics library_version: 8.0.23 inference: false datasets: - keremberke/hard-hat-detection model-index: - name: keremberke/yolov8m-hard-hat-detection results: - task: type: object-detection dataset: type: keremberke/hard-hat-detection name: hard-hat-detection split: validation metrics: - type: precision # since mAP@0.5 is not available on hf.co/metrics value: 0.81115 # min: 0.0 - max: 1.0 name: mAP@0.5(box) --- <div align="center"> <img width="640" alt="keremberke/yolov8m-hard-hat-detection" src="https://huggingface.co/keremberke/yolov8m-hard-hat-detection/resolve/main/thumbnail.jpg"> </div> ### Supported Labels ``` ['Hardhat', 'NO-Hardhat'] ``` ### 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, render_result # load model model = YOLO('keremberke/yolov8m-hard-hat-detection') # 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() ``` **More models available at: [awesome-yolov8-models](https://yolov8.xyz)**

Source context: 15 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo jopn122/yolov8m-hard-hat-detection