h^BSSKJr SSKJr SSKJrJr "SS\5rg))Results)DetectionPredictor) DEFAULT_CFGopscN^\rSrSrSr\SS4U4SjjrU4SjrSrSr Sr U=r $) SegmentationPredictora A class extending the DetectionPredictor class for prediction based on a segmentation model. This class specializes in processing segmentation model outputs, handling both bounding boxes and masks in the prediction results. Attributes: args (dict): Configuration arguments for the predictor. model (torch.nn.Module): The loaded YOLO segmentation model. batch (list): Current batch of images being processed. Methods: postprocess: Apply non-max suppression and process segmentation detections. construct_results: Construct a list of result objects from predictions. construct_result: Construct a single result object from a prediction. Examples: >>> from ultralytics.utils import ASSETS >>> from ultralytics.models.yolo.segment import SegmentationPredictor >>> args = dict(model="yolo11n-seg.pt", source=ASSETS) >>> predictor = SegmentationPredictor(overrides=args) >>> predictor.predict_cli() NcH>[TU]XU5 SURlg)a Initialize the SegmentationPredictor with configuration, overrides, and callbacks. This class specializes in processing segmentation model outputs, handling both bounding boxes and masks in the prediction results. Args: cfg (dict): Configuration for the predictor. overrides (dict, optional): Configuration overrides that take precedence over cfg. _callbacks (list, optional): List of callback functions to be invoked during prediction. segmentN)super__init__argstask)selfcfg overrides _callbacks __class__s a/home/james-whalen/.local/lib/python3.13/site-packages/ultralytics/models/yolo/segment/predict.pyr SegmentationPredictor.__init__!s 4" cr>[US[5(aUSSOUSn[TU] USX#US9$)a Apply non-max suppression and process segmentation detections for each image in the input batch. Args: preds (tuple): Model predictions, containing bounding boxes, scores, classes, and mask coefficients. img (torch.Tensor): Input image tensor in model format, with shape (B, C, H, W). orig_imgs (list | torch.Tensor | np.ndarray): Original image or batch of images. Returns: (list): List of Results objects containing the segmentation predictions for each image in the batch. Each Results object includes both bounding boxes and segmentation masks. Examples: >>> predictor = SegmentationPredictor(overrides=dict(model="yolo11n-seg.pt")) >>> results = predictor.postprocess(preds, img, orig_img) r)protos) isinstancetupler postprocess)rpredsimg orig_imgsrrs rr!SegmentationPredictor.postprocess0sC$",E!He!rLs!/E.jh.jhr