> Logs model info. Args: verbose (bool): Controls verbosity.

> Sends the model to the given device. Args: device (str): device

Prints model information Args: verbose: if True, prints out the model information. Defaults to False imgsz: the

Concatenates a list of Boxes into a single Bboxes Arguments: boxes_list (list[Bboxes]) Returns: Bbo

> This function loads the weights of the model from a file Args: weights: The weights to load into the model.

`from_numpy` converts a numpy array to a tensor Args: x: the numpy array to convert

Args: results: tuple(p, r, ap, f1, ap_class)

Check current version against the required minimum version. Args: current (str): Current version. minimum (str): Required mi

Load YAML data from a file. Args: file (str, optional): File name. Default is 'data.yaml'. append_filename (bool): Add the Y

> Logs the given text to given ranks process if provided, otherwise logs to all ranks. Args" text (str): text to log

Verify image size is a multiple of the given stride in each dimension. If the image size is not a multiple of the stride, update it to the ne

Increments a file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc. If the path exists and exist_ok is not set t

> It converts the bounding box from x,y,w,h to x1,y1,x2,y2 where xy1=top-left, xy2=bottom-right Args: x: the input tensor Returns

Args: offset (tuple | List | int): the offset for four coords.

Load and merge configuration data from a file or dictionary. Args: config (Union[str, DictConfig]): Configuration data in the form o

Returns a loss dict with labelled training loss items tensor

Makes an HTTP request using the 'requests' library, with exponential backoff retries up to a specified timeout. Args: *args: Positio

> It takes a list of bounding boxes, and converts them from the format [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right

Save YAML data to a file. Args: file (str, optional): File name. Default is 'data.yaml'. data (dict, optional): Data to save

Check if the current script is running inside a Google Colab notebook. Returns: bool: True if running inside a Colab notebook, False

Mean of results, return mp, mr, map50, map

Visualize prediction. Args: source (str): Accepts all source types accepted by yolo **kwargs : Any other arg

Verify and split a 'api_key[sep]model_id' string, sep is one of '.' or '_' Args: key (str): The model key to split. If not provided,

> Trains the model on a given dataset. Args: **kwargs (Any): Any number of arguments representing the training configura

Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments:

Check a classification dataset such as Imagenet. Copy code This function takes a `dataset` name as input and returns a dictionary contai

class-aware result, return p[i], r[i], ap50[i], ap[i]

> It takes a mask and a bounding box, and returns a mask that is cropped to the bounding box Args: masks: [h, w, n] tensor of masks

Args: scale (tuple | List | int): the scale for four coords.

Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Ar

Sync analytics data if enabled in the global settings Args: cfg (DictConfig): Configuration for the task and mode. all_keys (

> It converts the normalized coordinates to the actual coordinates [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right Args: x: the

> Forward pass of the network Args: x: input to the model profile: if True, the time taken for each layer will b

Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Ar

Attempt to authenticate with server

Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2 box1: np.array of shape(nx4) box2: np.array of shape(

> Export model. Args: **kwargs : Any other args accepted by the predictors. To see all args check 'configuration' sectio

> It takes a model and fuses the Conv2d() and BatchNorm2d() layers into a single layer Returns: The model is being returne

Check if a string is composed of only ASCII characters. Args: s (str): String to be checked. Returns: bool: True if the

Generate anchors from features.

this might be similar with denormalize func but without normalized sign

Return correct prediction matrix Arguments: detections (array[N, 6]), x1, y1, x2, y2, conf, class labels (arr