> Perform non-maximum suppression (NMS) on a set of boxes, with support for masks and multiple labels per box. Arguments: prediction

Args: imgsz (tuple): The image size. polygons (list[np.ndarray]): each polygon is [N, M], N is number of polygons, M is number of

Return a (640, 640) overlap mask.

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> Allows custom preprocessing model inputs and ground truths depending on task type.

description: Read an image from image path, convert it to RGB, resize and pad it to target size, normalize to [0,1],

YOLOv5 speed/memory/FLOPs profiler Usage: input = torch.randn(16, 3, 640, 640) m1 = lambda x: x * torch.sigmoid(x) m2 = n

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> Rescale boxes (xyxy) from img1_shape to img0_shape Args: img1_shape: The shape of the image that the bounding boxes are for. bo

> It takes a mask, and resizes it to the original image size Args: im1_shape: model input shape, [h, w] masks: [h, w, num]

select the positive anchor center in gt Args: xy_centers (Tensor): shape(h*w, 4) gt_bboxes (Tensor): shape(b, n_boxes, 4) Ret

if an anchor box is assigned to multiple gts, the one with the highest iou will be selected. Args: mask_pos (Tensor): shape(b, n_

Args: metrics: (b, max_num_obj, h*w). topk_mask: (b, max_num_obj, topk) or None

To set or update model parameters before training.

> load/create/download model for any task.

include_class, filter labels to include only these classes (optional)

custom your label format here

> Runs validation on test set using self.validator. The returned dict is expected to contain "fitness" key.

> It converts the bounding box from [x, y, w, h] to [x1, y1, w, h] where xy1=top-left Args: x: the x coordinate of the center of the b

> Convert nx4 boxes from [x1, y1, x2, y2] to [x1, y1, w, h] where xy1=top-left, xy2=bottom-right Args: x: the input tensor Return

> It takes in a list of bounding boxes, and returns a list of bounding boxes, but with the x and y coordinates normalized to the width and he

\brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR Example usage: LOG_ERROR(logger) << "hello world" <<

\brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR ("fatal" severity) Example usage: LOG_FATAL(

\brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO Example usage: LOG_INFO(logger) << "hello world" << st

\brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE Example usage: LOG_VERBOSE(logger) << "hello world"

\brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING Example usage: LOG_WARN(logger) << "hello world" <<

\brief Creates a LogStreamConsumer which logs messages with level severity. Reportable severity determines if the messages are severe enough to be log

Affine images and targets. Args: labels(Dict): a dict of `bboxes`, `segments`, `keypoints`.