ó œþöh’ ãó:•SSKrSSKJr SSKJr "SS\5rg)éN)ÚDetectionPredictor)Úopscó,^•\rSrSrSrU4SjrSrU=r$)Ú NASPredictoré aÿ Ultralytics YOLO NAS Predictor for object detection. This class extends the DetectionPredictor from Ultralytics engine and is responsible for post-processing the raw predictions generated by the YOLO NAS models. It applies operations like non-maximum suppression and scaling the bounding boxes to fit the original image dimensions. Attributes: args (Namespace): Namespace containing various configurations for post-processing including confidence threshold, IoU threshold, agnostic NMS flag, maximum detections, and class filtering options. model (torch.nn.Module): The YOLO NAS model used for inference. batch (list): Batch of inputs for processing. Examples: >>> from ultralytics import NAS >>> model = NAS("yolo_nas_s") >>> predictor = model.predictor Assume that raw_preds, img, orig_imgs are available >>> results = predictor.postprocess(raw_preds, img, orig_imgs) Notes: Typically, this class is not instantiated directly. It is used internally within the NAS class. cóº>•[R"USS5n[R"XASS4S5R SSS5n[ TU]XRU5$)a’ Postprocess NAS model predictions to generate final detection results. This method takes raw predictions from a YOLO NAS model, converts bounding box formats, and applies post-processing operations to generate the final detection results compatible with Ultralytics result visualization and analysis tools. Args: preds_in (list): Raw predictions from the NAS model, typically containing bounding boxes and class scores. img (torch.Tensor): Input image tensor that was fed to the model, with shape (B, C, H, W). orig_imgs (list | torch.Tensor | np.ndarray): Original images before preprocessing, used for scaling coordinates back to original dimensions. Returns: (list): List of Results objects containing the processed predictions for each image in the batch. Examples: >>> predictor = NAS("yolo_nas_s").predictor >>> results = predictor.postprocess(raw_preds, img, orig_imgs) rééÿÿÿÿé)rÚ xyxy2xywhÚtorchÚcatÚpermuteÚsuperÚ postprocess)ÚselfÚpreds_inÚimgÚ orig_imgsÚboxesÚpredsÚ __class__s €ÚX/home/james-whalen/.local/lib/python3.13/site-packages/ultralytics/models/nas/predict.pyrÚNASPredictor.postprocess#sWø€ô*— ’ ˜h q™k¨!™nÓ-ˆÜ— ’ ˜5¨1¡+¨a¡.Ð1°2Ó6×>Ñ>¸qÀ!ÀQÓGˆÜ‰wÑ" 5¨yÓ9Ð9ó©)Ú__name__Ú __module__Ú __qualname__Ú__firstlineno__Ú__doc__rÚ__static_attributes__Ú __classcell__)rs@rrr sø†ñ÷2:ó:rr)r Ú&ultralytics.models.yolo.detect.predictrÚultralytics.utilsrrrrrÚr&sðó åEÝ!ô1:Ð%õ1:r