imgs, scale, scale is in radians

imgs, scale, scale is in radians

imgs, scale, scale is in radians

imgs, scale, scale is in radians

imgs, scale, scale is in radians

imgs, scale, scale is in radians

imgs, scale, scale is in radians

imgs, scale, scale is in radians

Defines the default arg scope for inception models. Args: weight_decay: The weight decay to use for regularizing the model. use_batch_norm:

Creates the Inception Resnet V2 model. Args: inputs: a 4-D tensor of size [batch_size, height, width, 3]. num_classes: number of predicted

Returns the scope with the default parameters for inception_resnet_v2. Args: weight_decay: the weight decay for weights variables. batch_no

Defines the Inception V1 architecture. This architecture is defined in: Going deeper with convolutions Christian Szegedy, Wei Liu, Yangqin

Inception v2 model for classification. Constructs an Inception v2 network for classification as described in http://arxiv.org/abs/1502.03167.

Inception model from http://arxiv.org/abs/1512.00567. "Rethinking the Inception Architecture for Computer Vision" Christian Szegedy, Vincent Van

Creates the Inception V4 model. Args: inputs: a 4-D tensor of size [batch_size, height, width, 3]. num_classes: number of predicted classes

Creates a variant of the LeNet model. Note that since the output is a set of 'logits', the values fall in the interval of (-infinity, infinity).

Defines the default lenet argument scope. Args: weight_decay: The weight decay to use for regularizing the model. Returns: An `arg_scope

Mobilenet v1 model for classification. Args: inputs: a tensor of shape [batch_size, height, width, channels]. num_classes: number of predic

Defines the default MobilenetV1 arg scope. Args: is_training: Whether or not we're training the model. weight_decay: The weight decay to us

Contains the model definition for the OverFeat network. The definition for the network was obtained from: OverFeat: Integrated Recognition, Loc

Defines the default ResNet arg scope. TODO(gpapan): The batch-normalization related default values above are appropriate for use in conjunction

ResNet-101 model of [1]. See resnet_v1() for arg and return description.

ResNet-152 model of [1]. See resnet_v1() for arg and return description.

ResNet-200 model of [2]. See resnet_v1() for arg and return description.

ResNet-50 model of [1]. See resnet_v1() for arg and return description.

ResNet-101 model of [1]. See resnet_v2() for arg and return description.

ResNet-152 model of [1]. See resnet_v2() for arg and return description.

ResNet-200 model of [2]. See resnet_v2() for arg and return description.

ResNet-50 model of [1]. See resnet_v2() for arg and return description.

Stacks ResNet `Blocks` and controls output feature density. First, this function creates scopes for the ResNet in the form of 'block_name/unit_1'

Subsamples the input along the spatial dimensions. Args: inputs: A `Tensor` of size [batch, height_in, width_in, channels]. factor: The sub

Verify dense feature extraction with atrous convolution.

Verify dense feature extraction with atrous convolution.

Verify the values of dense feature extraction by atrous convolution. Make sure that dense feature extraction by stack_blocks_dense() followed by

Verify the values of dense feature extraction by atrous convolution. Make sure that dense feature extraction by stack_blocks_dense() followed by