--- tags: - ultralyticsplus - yolov8 - ultralytics - yolo - vision - image-segmentation - pytorch - awesome-yolov8-models library_name: ultralytics library_version: 8.0.20 inference: false datasets: - keremberke/satellite-building-segmentation model-index: - name: keremberke/yolov8m-building-segmentation results: - task: type: image-segmentation dataset: type: keremberke/satellite-building-segmentation name: satellite-building-segmentation split: validation metrics: - type: precision # since mAP@0.5 is not available on hf.co/metrics value: 0.62261 # min: 0.0 - max: 1.0 name: mAP@0.5(box) - type: precision # since mAP@0.5 is not available on hf.co/metrics value: 0.61275 # min: 0.0 - max: 1.0 name: mAP@0.5(mask) ---
keremberke/yolov8m-building-segmentation
### Supported Labels ``` ['Building'] ``` ### How to use - Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus): ```bash pip install ultralyticsplus==0.0.21 ``` - Load model and perform prediction: ```python from ultralyticsplus import YOLO, render_result # load model model = YOLO('keremberke/yolov8m-building-segmentation') # 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) print(results[0].masks) render = render_result(model=model, image=image, result=results[0]) render.show() ``` **More models available at: [awesome-yolov8-models](https://yolov8.xyz)**