This gives the gist of it. Not super important because it doesn't change as much

Creates a basic transformation that was used to train the models

r"""Loads the Torch serialized object at the given URL. If the object is already present in `model_dir`, it's deserialized and returned. The f

Arguments: original_image (np.ndarray): an image as returned by OpenCV Returns: prediction (BoxList): the de

Create a montage showing the probability heatmaps for each one one of the detected objects Arguments: image (np.

Cropss a rectangular region from this bounding box. The box is a 4-tuple defining the left, upper, right, and lower pixel coo

Evaluate detection proposal recall metrics. This function is a much faster alternative to the official COCO API recall evaluation code. However,

Returns bounding-box detection results by thresholding on scores and applying non-maximum suppression (NMS).

Arguments: x (Tensor): the mask logits boxes (list[BoxList]): bounding boxes that are used as referen

Arguments: anchors: list[BoxList] box_cls: tensor of size N, A * C, H, W box_regression: tensor of size N

Arguments: anchors: list[BoxList] box_cls: tensor of size N, A * C, H, W box_regression: tensor of size N

Arguments: anchors: list[BoxList] objectness: tensor of size N, A, H, W box_regression: tensor of size N,

Given a set of BoxList containing the `labels` field, return a set of BoxList for which `labels > 0`. Arguments: boxes (list of

Adds the predicted boxes on top of the image Arguments: image (np.ndarray): an image as returned by OpenCV p

Adds detected class names and scores in the positions defined by the top-left corner of the predicted bounding box Arguments

Adds the instances contours for each predicted object. Each label has a different color. Arguments: image (np.nd

Returns BoxList from `boxes` and adds probability scores information as an extra field `boxes` has shape (#detections, 4 * #c

Given segmentation masks and the bounding boxes corresponding to the location of the masks in the image, this function crops and resizes

Reduce the loss dictionary from all processes so that process with rank 0 has the averaged results. Returns a dict with the same fields as

Arguments: image (np.ndarray): an image as returned by OpenCV Returns: prediction (BoxList): the detected ob

This function gathers data from multiple processes, and returns them in a list, as they were obtained from each process. This function i

Select only predictions which have a `score` > self.confidence_threshold, and returns the predictions in descending order of score

Produce additional matches for predictions that have only low-quality matches. Specifically, for each ground-truth find the set of pr

Load custom environment setup from a Python source file and run the setup function.

Perform environment setup work. The default setup is a no-op, but this function allows the user to specify a Python source file that performs

This method performs the positive/negative sampling, and return the sampled proposals. Note: this function keeps a state.

Interface for Python

Interface for Python