Remove all boxes with any side smaller than min_size.

Balanced L1 Loss arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019)

default_evaluation_params: Default parameters to use for the validation and evaluation.

Method evaluate_method: evaluate method and returns the results Results. Dictionary with the following values: - method (required

Compute classification loss and smooth l1 loss. Args: confidence (batch_size, num_anchors, num_classes): class predicti

WARNING: The batch size of x must be 1.

text_proposals:boxes

Returns all points, confindences and transcriptions of a file in lists. Valid line formats: xmin,ymin,xmax,ymax,[confidence],[transcription]

Returns an array with the contents (filtered by fileNameRegExp) of a ZIP file. The key's are the names or the file or the capturing group def

Returns an array with the contents (filtered by fileNameRegExp) of a ZIP file. The key's are the names or the file or the capturing group def

Returns an array with the entries of the ZIP file that match with the regular expression. The key's are the names or the file or the capturin

This process validates a method, evaluates it and if it succed generates a ZIP file with a JSON entry for each sample. Params: p: Diction

This process validates a method Params: default_evaluation_params_fn: points to a function that returns a dictionary with the default par

Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-34 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ShuffleNetV2 with 0.5x output channels, as described in `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Des

Constructs a ShuffleNetV2 with 1.0x output channels, as described in `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Des

Constructs a ShuffleNetV2 with 1.5x output channels, as described in `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Des

Constructs a ShuffleNetV2 with 2.0x output channels, as described in `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Des

r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" <https://arxi

r"""SqueezeNet 1.1 model from the `official SqueezeNet repo <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_. SqueezeNe

Method validate_data: validates that all files in the results folder are correct (have the correct name contents). Va

This function validates that all lines of the file calling the Line validation function for each line

VGG 11-layer model (configuration "A") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 11-layer model (configuration "A") with batch normalization

VGG 13-layer model (configuration "B") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 13-layer model (configuration "B") with batch normalization

VGG 16-layer model (configuration "D") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 16-layer model (configuration "D") with batch normalization

VGG 19-layer model (configuration "E") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 19-layer model (configuration 'E') with batch normalization