With option to don't use hflip Returns: list[Augmentation]

Create evaluator(s) for a given dataset. This uses the special metadata "evaluator_type" associated with each builtin dataset.

Create a ResNet instance from config. Returns: ResNet: a :class:`ResNet` instance.

Create a ResNet instance from config. Returns: ResNet: a :class:`ResNet` instance.

1x1 convolution with padding

Create semantic segmentation annotations from panoptic segmentation annotations, to be used by PanopticFPN. It maps all thing categories

Create semantic segmentation annotations from panoptic segmentation annotations, to be used by PanopticFPN. It maps all thing categories

Returns a Unicode object on success, or None on failure

Apply the sigmoid operation.

Args: text (str): class label position (tuple): a tuple of the x and y coordinates to place text on image.

Apply dimensionality reduction to X. X is projected on the first principal components previously extracted from a training set.

evaluation_imports: Dictionary ( key = module name , value = alias ) with python modules used in the evaluation.

evaluation_imports: Dictionary ( key = module name , value = alias ) with python modules used in the evaluation.

Generate a CropTransform so that the cropping region contains the center of the given instance. Args: crop_size (tuple): h, w in

Resize image while keeping the aspect ratio. Args: img (ndarray): The input image. scale (float | tuple[int]): The scaling factor

Function used in Word Spotting that finds if the Ground Truth transcription meets the rules to enter into the dictionary. If not, the transcr

Function used in Word Spotting that finds if the Ground Truth transcription meets the rules to enter into the dictionary. If not, the transcr

Function applied to the Ground Truth transcriptions used in Word Spotting. It removes special characters or terminations

Function applied to the Ground Truth transcriptions used in Word Spotting. It removes special characters or terminations

Run inference on the given inputs. Args: batched_inputs (list[dict]): same as in :meth:`forward` detected_in

Apply the inverse sigmoid operation. y = -ln(1-x/x)

Transform data back to its original space. In other words, return an input X_original whose transform would be X. Parameters ----------

Load a json file with totaltext annotation format. Currently supports text detection and recognition. Args: json_file (str): ful

Return the losses from a set of FCOS predictions and their associated ground-truth. Returns: dict[loss name -> loss valu

Return the losses from a set of FCOS predictions and their associated ground-truth. Returns: dict[loss name -> loss valu

Return the losses from a set of MEInst predictions and their associated ground-truth. Returns: dict[loss name -> loss va