Loop through the registered actions and fire all callbacks on main thread Args: hook: The name of the hook to check,

Save the ONNX model to the given location. Args: output_path: Path pointing to the location where to write

save the metrics to the logging dictionary arguments: log_dict (Dict) -- metrics/media to be logged in current step

Parameters: 'dets' - a numpy array of detection in the format [[x1, y1, x2, y2, score], [x1,y1,x2,y2,score],...]

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

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

Non-Maximum Suppression (NMS) on inference results to reject overlapping detections Returns: list of detections, on (n,6) tensor per

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

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

"Crop" predicted masks by zeroing out everything not in the predicted bbox. Vectorized by Chong (thanks Chong). Args: - mas

source - https://gist.github.com/simon-weber/7853144 A context manager that will prevent any logging messages triggered during the body from bei

Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2 box1: np.array of shape(4) box2: np.array of shape