Reference for ultralytics/models/yolo/detect/predict.py

class DetectionPredictor(BasePredictor):

def construct_result(self, pred, img, orig_img, img_path):
    """Construct a single Results object from one image prediction.

    Args:
        pred (torch.Tensor): Predicted boxes and scores with shape (N, 6) where N is the number of detections.
        img (torch.Tensor): Preprocessed image tensor used for inference.
        orig_img (np.ndarray): Original image before preprocessing.
        img_path (str): Path to the original image file.

    Returns:
        (Results): Results object containing the original image, image path, class names, and scaled bounding boxes.
    """
    pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
    return Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6])

def construct_results(self, preds, img, orig_imgs):
    """Construct a list of Results objects from model predictions.

    Args:
        preds (list[torch.Tensor]): List of predicted bounding boxes and scores for each image.
        img (torch.Tensor): Batch of preprocessed images used for inference.
        orig_imgs (list[np.ndarray]): List of original images before preprocessing.

    Returns:
        (list[Results]): List of Results objects containing detection information for each image.
    """
    return [
        self.construct_result(pred, img, orig_img, img_path)
        for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0])
    ]

def get_obj_feats(self, feat_maps, idxs):
    """Extract object features from the feature maps."""
    import torch

    s = min(x.shape[1] for x in feat_maps)  # find shortest vector length
    obj_feats = torch.cat(
        [x.permute(0, 2, 3, 1).reshape(x.shape[0], -1, s, x.shape[1] // s).mean(dim=-1) for x in feat_maps], dim=1
    )  # mean reduce all vectors to same length
    return [feats[idx] if idx.shape[0] else [] for feats, idx in zip(obj_feats, idxs)]  # for each img in batch

def postprocess(self, preds, img, orig_imgs, **kwargs):
    """Post-process predictions and return a list of Results objects.

    This method applies non-maximum suppression to raw model predictions and prepares them for visualization and
    further analysis.

    Args:
        preds (torch.Tensor): Raw predictions from the model.
        img (torch.Tensor): Processed input image tensor in model input format.
        orig_imgs (torch.Tensor | list): Original input images before preprocessing.
        **kwargs (Any): Additional keyword arguments.

    Returns:
        (list): List of Results objects containing the post-processed predictions.

    Examples:
        >>> predictor = DetectionPredictor(overrides=dict(model="yolo11n.pt"))
        >>> results = predictor.predict("path/to/image.jpg")
        >>> processed_results = predictor.postprocess(preds, img, orig_imgs)
    """
    save_feats = getattr(self, "_feats", None) is not None
    preds = nms.non_max_suppression(
        preds,
        self.args.conf,
        self.args.iou,
        self.args.classes,
        self.args.agnostic_nms,
        max_det=self.args.max_det,
        nc=0 if self.args.task == "detect" else len(self.model.names),
        end2end=getattr(self.model, "end2end", False),
        rotated=self.args.task == "obb",
        return_idxs=save_feats,
    )

    if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
        orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)[..., ::-1]

    if save_feats:
        obj_feats = self.get_obj_feats(self._feats, preds[1])
        preds = preds[0]

    results = self.construct_results(preds, img, orig_imgs, **kwargs)

    if save_feats:
        for r, f in zip(results, obj_feats):
            r.feats = f  # add object features to results

    return results