Finds the ancestor of a TF tensor in the computation graph. tensor: TensorFlow symbolic tensor. name: Name of ancestor tensor to find

Applies the given deltas to the given boxes. boxes: [N, (y1, x1, y2, x2)]. Note that (y2, x2) is outside the box. deltas: [N, (dy, dx, log(dh)

Applies the given deltas to the given boxes. boxes: [N, (y1, x1, y2, x2)]. Note that (y2, x2) is outside the box. deltas: [N, (dy, dx, log(dh)

Applies the given deltas to the given boxes. boxes: [N, (y1, x1, y2, x2)]. Note that (y2, x2) is outside the box. deltas: [N, (dy, dx, log(dh)

Splits inputs into slices and feeds each slice to a copy of the given computation graph and then combines the results. It allows you to run a

Splits inputs into slices and feeds each slice to a copy of the given computation graph and then combines the results. It allows you to run a

Splits inputs into slices and feeds each slice to a copy of the given computation graph and then combines the results. It allows you to run a

Compute refinement needed to transform box to gt_box. box and gt_box are [N, (y1, x1, y2, x2)]. (y2, x2) is assumed to be outside the box.

Compute refinement needed to transform box to gt_box. box and gt_box are [N, (y1, x1, y2, x2)]. (y2, x2) is assumed to be outside the box.

Compute refinement needed to transform box to gt_box. box and gt_box are [N, (y1, x1, y2, x2)]. (y2, x2) is assumed to be outside the box.

Compute refinement needed to transform box to gt_box. box and gt_box are [N, (y1, x1, y2, x2)]

Compute refinement needed to transform box to gt_box. box and gt_box are [N, (y1, x1, y2, x2)]

Compute refinement needed to transform box to gt_box. box and gt_box are [N, (y1, x1, y2, x2)]

Note about training values: None: Train BN layers. This is the normal mode False: Freeze BN layers. Good when batch s

Note about training values: None: Train BN layers. This is the normal mode False: Freeze BN layers. Good when batch s

Numpy function to plot CDF.

Returns a shorter version of object names for cleaner display.

Returns a shorter version of object names for cleaner display.

Returns a shorter version of object names for cleaner display.

window: (y1, x1, y2, x2). The window in the image we want to clip to. boxes: [N, (y1, x1, y2, x2)]

Compute Average Precision at a set IoU threshold (default 0.5). Returns: mAP: Mean Average Precision precisions: List of precisions at di

Compute AP over a range or IoU thresholds. Default range is 0.5-0.95.

Compute AP over a range or IoU thresholds. Default range is 0.5-0.95.

Compute the recall at the given IoU threshold. It's an indication of how many GT boxes were found by the given prediction boxes. pred_boxes:

Compute the recall at the given IoU threshold. It's an indication of how many GT boxes were found by the given prediction boxes. pred_boxes:

Compute the recall at the given IoU threshold. It's an indication of how many GT boxes were found by the given prediction boxes. pred_boxes:

Reads a label map and returns a category index. Args: label_map_path: Path to `StringIntLabelMap` proto text file. Returns: A category i

Creates a category index with a single `object` class.

Converts boxes from normalized coordinates to pixel coordinates. boxes: [N, (y1, x1, y2, x2)] in normalized coordinates shape: [..., (height,

Converts boxes from normalized coordinates to pixel coordinates. boxes: [N, (y1, x1, y2, x2)] in normalized coordinates shape: [..., (height,

Converts boxes from normalized coordinates to pixel coordinates. boxes: [..., (y1, x1, y2, x2)] in normalized coordinates shape: [..., (height

Converts boxes from normalized coordinates to pixel coordinates. boxes: [..., (y1, x1, y2, x2)] in normalized coordinates shape: [..., (height

Runs the detection pipeline, but expect inputs that are molded already. Used mostly for debugging and inspecting the model. m

Runs the detection pipeline, but expect inputs that are molded already. Used mostly for debugging and inspecting the model. m

Display ground truth and prediction instances on the same image.

Display ground truth and prediction instances on the same image.

boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates. masks: [height, width, num_instances] class_ids: [num_instances]

boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates. masks: [height, width, num_instances] class_ids: [num_instances]

Display the given image and the top few class masks.

Display the given image and the top few class masks.

Display the given image and the top few class masks.

Display the given image and the top few class masks.

Scans all the weights in the model and returns a list of tuples that contain stats about each weight.

Scans all the weights in the model and returns a list of tuples that contain stats about each weight.

Scans all the weights in the model and returns a list of tuples that contain stats about each weight.

Scans all the weights in the model and returns a list of tuples that contain stats about each weight.

Download COCO trained weights from Releases. coco_model_path: local path of COCO trained weights

Download COCO trained weights from Releases. coco_model_path: local path of COCO trained weights

Download COCO trained weights from Releases. coco_model_path: local path of COCO trained weights

Draws bounding boxes on image (numpy array). Args: image: a numpy array object. boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin,

Draws bounding boxes on batch of image tensors. Args: images: A 4D uint8 image tensor of shape [N, H, W, C]. boxes: [N, max_detections, 4]

Draw 3-pixel width bounding boxes on the given image array. color: list of 3 int values for RGB.

Draw 3-pixel width bounding boxes on the given image array. color: list of 3 int values for RGB.

Draw 3-pixel width bounding boxes on the given image array. color: list of 3 int values for RGB.

Draw 3-pixel width bounding boxes on the given image array. color: list of 3 int values for RGB.

Draws boxes on image.

Draw bounding boxes and segmentation masks with differnt customizations. boxes: [N, (y1, x1, y2, x2, class_id)] in image coordinates. ref