List all the annotations of a sample. :param sample_token: Sample token. :param verbose: Whether to print to stdout.

List all attributes and the number of annotations with each attribute. :param sort_by: Sorting criteria, e.g. "name", "freq".

List all cameras and the number of samples for each.

List all categories and the number of object_anns and surface_anns for them. :param sample_tokens: A list of sample tokens for which

List all logs and the number of samples per log.

List the sample_datas for a given sample. :param sample_token: Sample token.

Show a histogram of the number of sample_datas per sample.

Classification loss (NLL) targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]

Compute the losses related to the masks: the focal loss and the dice loss. targets dicts must contain the key "masks" containing a tensor of d

More memory-friendly matching

Removes small disconnected regions and holes in masks, then reruns box NMS to remove any new duplicates. Edits mask_data in

Predict masks for the given input prompts, using the currently set image. Arguments: point_coords (np.ndarray or None): A

This function is very similar to predict(...), however it is used for batched mode, when the model is expected to generate predictions on multiple ima

Propagate the input points across frames to track in the entire video.

Args: input_dict (dict): all the values will be reduced average (bool): whether to do average or sum Reduce the values in the

Remove an object id from the tracking state. If strict is True, we check whether the object id actually exists and raise an error if

Remove an object id from the tracking state. If strict is True, we check whether the object id actually exists and raise an error if

Renders an image (sample_data), optionally with annotations overlaid. :param sd_token: The token of the sample_data to be rendered.

Render a plot of the trajectory for the clip surrounding the annotated keyframe. A red cross indicates the starting point, a green do

Sample a noised version of the top left and bottom right corners of a given `bbox` Inputs: - masks: [B, 1, H,W] boxes, dtype=torch.Tenso

Calculates the image embeddings for the provided image batch, allowing masks to be predicted with the 'predict_batch' method.

Args: inputs: A float tensor of arbitrary shape. The predictions for each example. targets: A float tensor with t

Args: inputs: A float tensor of arbitrary shape. The predictions for each example. targets: A float tensor with t

Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Args: inputs: A float tensor of arbitrary shape.

Returns the folder where the tables are stored for the relevant version.

Convert the model into training mode while keep layers freezed.

Convert the model into training mode while keep layers freezed.

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