package caffe

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Used in: LayerParameter, V1LayerParameter

• optional uint32 top_k = 1

  When computing accuracy, count as correct by comparing the true label to the top k scoring classes. By default, only compare to the top scoring class (i.e. argmax).

• optional int32 axis = 2

  The "label" axis of the prediction blob, whose argmax corresponds to the predicted label -- may be negative to index from the end (e.g., -1 for the last axis). For example, if axis == 1 and the predictions are (N x C x H x W), the label blob is expected to contain N*H*W ground truth labels with integer values in {0, 1, ..., C-1}.

• optional int32 ignore_label = 3

  If specified, ignore instances with the given label.

Used in: LayerParameter, V1LayerParameter

• optional bool out_max_val = 1

  If true produce pairs (argmax, maxval)

• optional uint32 top_k = 2

• optional int32 axis = 3

  The axis along which to maximise -- may be negative to index from the end (e.g., -1 for the last axis). By default ArgMaxLayer maximizes over the flattened trailing dimensions for each index of the first / num dimension.

Used in: LayerParameter

• optional bool use_global_stats = 1

  If false, accumulate global mean/variance values via a moving average. If true, use those accumulated values instead of computing mean/variance across the batch.

• optional float moving_average_fraction = 2

  How much does the moving average decay each iteration?

• optional float eps = 3

  Small value to add to the variance estimate so that we don't divide by zero.

Used in: LayerParameter

• optional int32 axis = 1

  The first axis of bottom[0] (the first input Blob) along which to apply bottom[1] (the second input Blob). May be negative to index from the end (e.g., -1 for the last axis). For example, if bottom[0] is 4D with shape 100x3x40x60, the output top[0] will have the same shape, and bottom[1] may have any of the following shapes (for the given value of axis): (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60 (axis == 1 == -3) 3; 3x40; 3x40x60 (axis == 2 == -2) 40; 40x60 (axis == 3 == -1) 60 Furthermore, bottom[1] may have the empty shape (regardless of the value of "axis") -- a scalar bias.

• optional int32 num_axes = 2

  (num_axes is ignored unless just one bottom is given and the bias is a learned parameter of the layer. Otherwise, num_axes is determined by the number of axes by the second bottom.) The number of axes of the input (bottom[0]) covered by the bias parameter, or -1 to cover all axes of bottom[0] starting from `axis`. Set num_axes := 0, to add a zero-axis Blob: a scalar.

• optional FillerParameter filler = 3

  (filler is ignored unless just one bottom is given and the bias is a learned parameter of the layer.) The initialization for the learned bias parameter. Default is the zero (0) initialization, resulting in the BiasLayer initially performing the identity operation.

Used in: BlobProtoVector, LayerParameter, SolverState, V0LayerParameter, V1LayerParameter

• optional BlobShape shape = 7

• repeated float data = 5

• repeated float diff = 6

• repeated double double_data = 8

• repeated double double_diff = 9

• optional int32 num = 1

  4D dimensions -- deprecated. Use "shape" instead.

• optional int32 channels = 2

• optional int32 height = 3

• optional int32 width = 4

The BlobProtoVector is simply a way to pass multiple blobproto instances around.

• repeated BlobProto blobs = 1

Specifies the shape (dimensions) of a Blob.

Used in: BlobProto, DummyDataParameter, InputParameter, NetParameter, ParameterParameter, ReshapeParameter

• repeated int64 dim = 1

Used in: LayerParameter

• required uint32 roi_per_img = 1

  number of rois for training

• optional int32 ignore_label = 2

  ignore_label in scoring

Used in: LayerParameter, V1LayerParameter

• optional int32 axis = 2

  The axis along which to concatenate -- may be negative to index from the end (e.g., -1 for the last axis). Other axes must have the same dimension for all the bottom blobs. By default, ConcatLayer concatenates blobs along the "channels" axis (1).

• optional uint32 concat_dim = 1

  DEPRECATED: alias for "axis" -- does not support negative indexing.

Used in: LayerParameter, V1LayerParameter

• optional float margin = 1

  margin for dissimilar pair

• optional bool legacy_version = 2

  The first implementation of this cost did not exactly match the cost of Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2. legacy_version = false (the default) uses (margin - d)^2 as proposed in the Hadsell paper. New models should probably use this version. legacy_version = true uses (margin - d^2). This is kept to support / reproduce existing models and results

Used in: LayerParameter, V1LayerParameter

• optional uint32 num_output = 1

  The number of outputs for the layer

• optional bool bias_term = 2

  whether to have bias terms

• repeated uint32 pad = 3

  Pad, kernel size, and stride are all given as a single value for equal dimensions in all spatial dimensions, or once per spatial dimension.

  The padding size; defaults to 0

• repeated uint32 kernel_size = 4

  The kernel size

• repeated uint32 stride = 6

  The stride; defaults to 1

• repeated uint32 dilation = 18

  Factor used to dilate the kernel, (implicitly) zero-filling the resulting holes. (Kernel dilation is sometimes referred to by its use in the algorithme à trous from Holschneider et al. 1987.)

  The dilation; defaults to 1

• optional uint32 pad_h = 9

  For 2D convolution only, the *_h and *_w versions may also be used to specify both spatial dimensions.

  The padding height (2D only)

• optional uint32 pad_w = 10

  The padding width (2D only)

• optional uint32 kernel_h = 11

  The kernel height (2D only)

• optional uint32 kernel_w = 12

  The kernel width (2D only)

• optional uint32 stride_h = 13

  The stride height (2D only)

• optional uint32 stride_w = 14

  The stride width (2D only)

• optional uint32 group = 5

  The group size for group conv

• optional FillerParameter weight_filler = 7

  The filler for the weight

• optional FillerParameter bias_filler = 8

  The filler for the bias

• optional ConvolutionParameter.Engine engine = 15

• optional int32 axis = 16

  The axis to interpret as "channels" when performing convolution. Preceding dimensions are treated as independent inputs; succeeding dimensions are treated as "spatial". With (N, C, H, W) inputs, and axis == 1 (the default), we perform N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for groups g>1) filters across the spatial axes (H, W) of the input. With (N, C, D, H, W) inputs, and axis == 1, we perform N independent 3D convolutions, sliding (C/g)-channels filters across the spatial axes (D, H, W) of the input.

• optional bool force_nd_im2col = 17

  Whether to force use of the general ND convolution, even if a specific implementation for blobs of the appropriate number of spatial dimensions is available. (Currently, there is only a 2D-specific convolution implementation; for input blobs with num_axes != 2, this option is ignored and the ND implementation will be used.)

Used in: ConvolutionParameter

• DEFAULT = 0

• CAFFE = 1

• CUDNN = 2

Used in: LayerParameter

• optional int32 axis = 1

  To crop, elements of the first bottom are selected to fit the dimensions of the second, reference bottom. The crop is configured by - the crop `axis` to pick the dimensions for cropping - the crop `offset` to set the shift for all/each dimension to align the cropped bottom with the reference bottom. All dimensions up to but excluding `axis` are preserved, while the dimensions including and trailing `axis` are cropped. If only one `offset` is set, then all dimensions are offset by this amount. Otherwise, the number of offsets must equal the number of cropped axes to shift the crop in each dimension accordingly. Note: standard dimensions are N,C,H,W so the default is a spatial crop, and `axis` may be negative to index from the end (e.g., -1 for the last axis).

• repeated uint32 offset = 2

Used in: LayerParameter, V1LayerParameter

• optional string source = 1

  Specify the data source.

• optional uint32 batch_size = 4

  Specify the batch size.

• optional uint32 rand_skip = 7

  The rand_skip variable is for the data layer to skip a few data points to avoid all asynchronous sgd clients to start at the same point. The skip point would be set as rand_skip * rand(0,1). Note that rand_skip should not be larger than the number of keys in the database. DEPRECATED. Each solver accesses a different subset of the database.

• optional DataParameter.DB backend = 8

• optional float scale = 2

  DEPRECATED. See TransformationParameter. For data pre-processing, we can do simple scaling and subtracting the data mean, if provided. Note that the mean subtraction is always carried out before scaling.

• optional string mean_file = 3

• optional uint32 crop_size = 5

  DEPRECATED. See TransformationParameter. Specify if we would like to randomly crop an image.

• optional bool mirror = 6

  DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror data.

• optional bool force_encoded_color = 9

  Force the encoded image to have 3 color channels

• optional uint32 prefetch = 10

  Prefetch queue (Increase if data feeding bandwidth varies, within the limit of device memory for GPU training)

Used in: DataParameter

• LEVELDB = 0

• LMDB = 1

• optional int32 channels = 1

• optional int32 height = 2

• optional int32 width = 3

• optional bytes data = 4

  the actual image data, in bytes

• optional int32 label = 5

• repeated float float_data = 6

  Optionally, the datum could also hold float data.

• optional bool encoded = 7

  If true data contains an encoded image that need to be decoded

Used in: LayerParameter, V1LayerParameter

• optional float dropout_ratio = 1

  dropout ratio

DummyDataLayer fills any number of arbitrarily shaped blobs with random (or constant) data generated by "Fillers" (see "message FillerParameter").

Used in: LayerParameter, V1LayerParameter

• repeated FillerParameter data_filler = 1

  This layer produces N >= 1 top blobs. DummyDataParameter must specify 1 or N shape fields, and 0, 1 or N data_fillers. If 0 data_fillers are specified, ConstantFiller with a value of 0 is used. If 1 data_filler is specified, it is applied to all top blobs. If N are specified, the ith is applied to the ith top blob.

• repeated BlobShape shape = 6

• repeated uint32 num = 2

  4D dimensions -- deprecated. Use "shape" instead.

• repeated uint32 channels = 3

• repeated uint32 height = 4

• repeated uint32 width = 5

Message that stores parameters used by ELULayer

Used in: LayerParameter

• optional float alpha = 1

  Described in: Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). arXiv

Used in: LayerParameter, V1LayerParameter

• optional EltwiseParameter.EltwiseOp operation = 1

  element-wise operation

• repeated float coeff = 2

  blob-wise coefficient for SUM operation

• optional bool stable_prod_grad = 3

  Whether to use an asymptotically slower (for >2 inputs) but stabler method of computing the gradient for the PROD operation. (No effect for SUM op.)

Used in: EltwiseParameter

• PROD = 0

• SUM = 1

• MAX = 2

Message that stores parameters used by EmbedLayer

Used in: LayerParameter

• optional uint32 num_output = 1

  The number of outputs for the layer

• optional uint32 input_dim = 2

  The input is given as integers to be interpreted as one-hot vector indices with dimension num_input. Hence num_input should be 1 greater than the maximum possible input value.

• optional bool bias_term = 3

  Whether to use a bias term

• optional FillerParameter weight_filler = 4

  The filler for the weight

• optional FillerParameter bias_filler = 5

  The filler for the bias

Message that stores parameters used by ExpLayer

Used in: LayerParameter, V1LayerParameter

• optional float base = 1

  ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0. Or if base is set to the default (-1), base is set to e, so y = exp(shift + scale * x).

• optional float scale = 2

• optional float shift = 3

Used in: BiasParameter, ConvolutionParameter, DummyDataParameter, EmbedParameter, InnerProductParameter, PReLUParameter, RecurrentParameter, ScaleParameter, V0LayerParameter

• optional string type = 1

  The filler type.

• optional float value = 2

  the value in constant filler

• optional float min = 3

  the min value in uniform filler

• optional float max = 4

  the max value in uniform filler

• optional float mean = 5

  the mean value in Gaussian filler

• optional float std = 6

  the std value in Gaussian filler

• optional int32 sparse = 7

  The expected number of non-zero output weights for a given input in Gaussian filler -- the default -1 means don't perform sparsification.

• optional FillerParameter.VarianceNorm variance_norm = 8

Normalize the filler variance by fan_in, fan_out, or their average. Applies to 'xavier' and 'msra' fillers.

Used in: FillerParameter

• FAN_IN = 0

• FAN_OUT = 1

• AVERAGE = 2

/ Message that stores parameters used by FlattenLayer

Used in: LayerParameter

• optional int32 axis = 1

  The first axis to flatten: all preceding axes are retained in the output. May be negative to index from the end (e.g., -1 for the last axis).

• optional int32 end_axis = 2

  The last axis to flatten: all following axes are retained in the output. May be negative to index from the end (e.g., the default -1 for the last axis).

Message that stores parameters used by HDF5DataLayer

Used in: LayerParameter, V1LayerParameter

• optional string source = 1

  Specify the data source.

• optional uint32 batch_size = 2

  Specify the batch size.

• optional bool shuffle = 3

  Specify whether to shuffle the data. If shuffle == true, the ordering of the HDF5 files is shuffled, and the ordering of data within any given HDF5 file is shuffled, but data between different files are not interleaved; all of a file's data are output (in a random order) before moving onto another file.

Used in: LayerParameter, V0LayerParameter, V1LayerParameter

• optional string file_name = 1

Used in: LayerParameter, V1LayerParameter

• optional HingeLossParameter.Norm norm = 1

  Specify the Norm to use L1 or L2

Used in: HingeLossParameter

• L1 = 1

• L2 = 2

Used in: LayerParameter, V1LayerParameter

• optional string source = 1

  Specify the data source.

• optional uint32 batch_size = 4

  Specify the batch size.

• optional uint32 rand_skip = 7

  The rand_skip variable is for the data layer to skip a few data points to avoid all asynchronous sgd clients to start at the same point. The skip point would be set as rand_skip * rand(0,1). Note that rand_skip should not be larger than the number of keys in the database.

• optional bool shuffle = 8

  Whether or not ImageLayer should shuffle the list of files at every epoch.

• optional uint32 new_height = 9

  It will also resize images if new_height or new_width are not zero.

• optional uint32 new_width = 10

• optional bool is_color = 11

  Specify if the images are color or gray

• optional float scale = 2

  DEPRECATED. See TransformationParameter. For data pre-processing, we can do simple scaling and subtracting the data mean, if provided. Note that the mean subtraction is always carried out before scaling.

• optional string mean_file = 3

• optional uint32 crop_size = 5

  DEPRECATED. See TransformationParameter. Specify if we would like to randomly crop an image.

• optional bool mirror = 6

  DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror data.

• optional string root_folder = 12

Used in: LayerParameter, V1LayerParameter

• optional string source = 1

  Specify the infogain matrix source.

Used in: LayerParameter

• optional bool transpose = 1

Used in: LayerParameter, V1LayerParameter

• optional uint32 num_output = 1

  The number of outputs for the layer

• optional bool bias_term = 2

  whether to have bias terms

• optional FillerParameter weight_filler = 3

  The filler for the weight

• optional FillerParameter bias_filler = 4

  The filler for the bias

• optional int32 axis = 5

  The first axis to be lumped into a single inner product computation; all preceding axes are retained in the output. May be negative to index from the end (e.g., -1 for the last axis).

• optional bool transpose = 6

  Specify whether to transpose the weight matrix or not. If transpose == true, any operations will be performed on the transpose of the weight matrix. The weight matrix itself is not going to be transposed but rather the transfer flag of operations will be toggled accordingly.

Used in: LayerParameter

• repeated BlobShape shape = 1

  This layer produces N >= 1 top blob(s) to be assigned manually. Define N shapes to set a shape for each top. Define 1 shape to set the same shape for every top. Define no shape to defer to reshaping manually.

Message that stores parameters used by LRNLayer

Used in: LayerParameter, V1LayerParameter

• optional uint32 local_size = 1

• optional float alpha = 2

• optional float beta = 3

• optional LRNParameter.NormRegion norm_region = 4

• optional float k = 5

• optional LRNParameter.Engine engine = 6

Used in: LRNParameter

• DEFAULT = 0

• CAFFE = 1

• CUDNN = 2

Used in: LRNParameter

• ACROSS_CHANNELS = 0

• WITHIN_CHANNEL = 1

NOTE Update the next available ID when you add a new LayerParameter field. LayerParameter next available layer-specific ID: 150 (last added: recurrent_param)

Used in: NetParameter

• optional string name = 1

  the layer name

• optional string type = 2

  the layer type

• repeated string bottom = 3

  the name of each bottom blob

• repeated string top = 4

  the name of each top blob

• optional Phase phase = 10

  The train / test phase for computation.

• repeated float loss_weight = 5

  The amount of weight to assign each top blob in the objective. Each layer assigns a default value, usually of either 0 or 1, to each top blob.

• repeated ParamSpec param = 6

  Specifies training parameters (multipliers on global learning constants, and the name and other settings used for weight sharing).

• repeated BlobProto blobs = 7

  The blobs containing the numeric parameters of the layer.

• repeated bool propagate_down = 11

  Specifies whether to backpropagate to each bottom. If unspecified, Caffe will automatically infer whether each input needs backpropagation to compute parameter gradients. If set to true for some inputs, backpropagation to those inputs is forced; if set false for some inputs, backpropagation to those inputs is skipped. The size must be either 0 or equal to the number of bottoms.

• repeated NetStateRule include = 8

  Rules controlling whether and when a layer is included in the network, based on the current NetState. You may specify a non-zero number of rules to include OR exclude, but not both. If no include or exclude rules are specified, the layer is always included. If the current NetState meets ANY (i.e., one or more) of the specified rules, the layer is included/excluded.

• repeated NetStateRule exclude = 9

• optional TransformationParameter transform_param = 100

  Parameters for data pre-processing.

• optional LossParameter loss_param = 101

  Parameters shared by loss layers.

• optional AccuracyParameter accuracy_param = 102

  Layer type-specific parameters. Note: certain layers may have more than one computational engine for their implementation. These layers include an Engine type and engine parameter for selecting the implementation. The default for the engine is set by the ENGINE switch at compile-time.

• optional ArgMaxParameter argmax_param = 103

• optional BatchNormParameter batch_norm_param = 139

• optional BiasParameter bias_param = 141

• optional ConcatParameter concat_param = 104

• optional ContrastiveLossParameter contrastive_loss_param = 105

• optional ConvolutionParameter convolution_param = 106

• optional CropParameter crop_param = 144

• optional DataParameter data_param = 107

• optional DropoutParameter dropout_param = 108

• optional DummyDataParameter dummy_data_param = 109

• optional EltwiseParameter eltwise_param = 110

• optional ELUParameter elu_param = 140

• optional EmbedParameter embed_param = 137

• optional ExpParameter exp_param = 111

• optional FlattenParameter flatten_param = 135

• optional HDF5DataParameter hdf5_data_param = 112

• optional HDF5OutputParameter hdf5_output_param = 113

• optional HingeLossParameter hinge_loss_param = 114

• optional ImageDataParameter image_data_param = 115

• optional InfogainLossParameter infogain_loss_param = 116

• optional InnerProductParameter inner_product_param = 117

• optional InputParameter input_param = 143

• optional LogParameter log_param = 134

• optional LRNParameter lrn_param = 118

• optional MemoryDataParameter memory_data_param = 119

• optional MVNParameter mvn_param = 120

• optional ParameterParameter parameter_param = 145

• optional PoolingParameter pooling_param = 121

• optional PowerParameter power_param = 122

• optional PReLUParameter prelu_param = 131

• optional PythonParameter python_param = 130

• optional RecurrentParameter recurrent_param = 146

• optional ReductionParameter reduction_param = 136

• optional ReLUParameter relu_param = 123

• optional ReshapeParameter reshape_param = 133

• optional ScaleParameter scale_param = 142

• optional SigmoidParameter sigmoid_param = 124

• optional SoftmaxParameter softmax_param = 125

• optional SPPParameter spp_param = 132

• optional SliceParameter slice_param = 126

• optional TanHParameter tanh_param = 127

• optional ThresholdParameter threshold_param = 128

• optional TileParameter tile_param = 138

• optional WindowDataParameter window_data_param = 129

• optional ROIPoolingParameter roi_pooling_param = 147

• optional PSROIPoolingParameter psroi_pooling_param = 148

• optional InnerProductBlobParameter inner_product_blob_param = 149

• optional SmoothL1LossParameter smooth_l1_loss_param = 150

• optional BoxAnnotatorOHEMParameter box_annotator_ohem_param = 151

Message that stores parameters used by LogLayer

Used in: LayerParameter

• optional float base = 1

  LogLayer computes outputs y = log_base(shift + scale * x), for base > 0. Or if base is set to the default (-1), base is set to e, so y = ln(shift + scale * x) = log_e(shift + scale * x)

• optional float scale = 2

• optional float shift = 3

Message that stores parameters shared by loss layers

Used in: LayerParameter, V1LayerParameter

• optional int32 ignore_label = 1

  If specified, ignore instances with the given label.

• optional LossParameter.NormalizationMode normalization = 3

  For historical reasons, the default normalization for SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.

• optional bool normalize = 2

  Deprecated. Ignored if normalization is specified. If normalization is not specified, then setting this to false will be equivalent to normalization = BATCH_SIZE to be consistent with previous behavior.

• optional float pre_fixed_normalizer = 4

How to normalize the loss for loss layers that aggregate across batches, spatial dimensions, or other dimensions. Currently only implemented in SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.

Used in: LossParameter

• FULL = 0

  Divide by the number of examples in the batch times spatial dimensions. Outputs that receive the ignore label will NOT be ignored in computing the normalization factor.

• VALID = 1

  Divide by the total number of output locations that do not take the ignore_label. If ignore_label is not set, this behaves like FULL.

• BATCH_SIZE = 2

  Divide by the batch size.

• PRE_FIXED = 3

  Divide by pre-fixed normalizer

• NONE = 4

  Do not normalize the loss.

Used in: LayerParameter, V1LayerParameter

• optional bool normalize_variance = 1

  This parameter can be set to false to normalize mean only

• optional bool across_channels = 2

  This parameter can be set to true to perform DNN-like MVN

• optional float eps = 3

  Epsilon for not dividing by zero while normalizing variance

Used in: LayerParameter, V1LayerParameter

• optional uint32 batch_size = 1

• optional uint32 channels = 2

• optional uint32 height = 3

• optional uint32 width = 4

Used in: SolverParameter

• optional string name = 1

  consider giving the network a name

• repeated string input = 3

  DEPRECATED. See InputParameter. The input blobs to the network.

• repeated BlobShape input_shape = 8

  DEPRECATED. See InputParameter. The shape of the input blobs.

• repeated int32 input_dim = 4

  4D input dimensions -- deprecated. Use "input_shape" instead. If specified, for each input blob there should be four values specifying the num, channels, height and width of the input blob. Thus, there should be a total of (4 * #input) numbers.

• optional bool force_backward = 5

  Whether the network will force every layer to carry out backward operation. If set False, then whether to carry out backward is determined automatically according to the net structure and learning rates.

• optional NetState state = 6

  The current "state" of the network, including the phase, level, and stage. Some layers may be included/excluded depending on this state and the states specified in the layers' include and exclude fields.

• optional bool debug_info = 7

  Print debugging information about results while running Net::Forward, Net::Backward, and Net::Update.

• repeated LayerParameter layer = 100

  The layers that make up the net. Each of their configurations, including connectivity and behavior, is specified as a LayerParameter.

  ID 100 so layers are printed last.

• repeated V1LayerParameter layers = 2

  DEPRECATED: use 'layer' instead.

Used in: NetParameter, SolverParameter

• optional Phase phase = 1

• optional int32 level = 2

• repeated string stage = 3

Used in: LayerParameter, V1LayerParameter

• optional Phase phase = 1

  Set phase to require the NetState have a particular phase (TRAIN or TEST) to meet this rule.

• optional int32 min_level = 2

  Set the minimum and/or maximum levels in which the layer should be used. Leave undefined to meet the rule regardless of level.

• optional int32 max_level = 3

• repeated string stage = 4

  Customizable sets of stages to include or exclude. The net must have ALL of the specified stages and NONE of the specified "not_stage"s to meet the rule. (Use multiple NetStateRules to specify conjunctions of stages.)

• repeated string not_stage = 5

Parametric ReLU described in K. He et al, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, 2015.

Used in: LayerParameter

• optional FillerParameter filler = 1

  Initial value of a_i. Default is a_i=0.25 for all i.

• optional bool channel_shared = 2

  Whether or not slope parameters are shared across channels.

Used in: LayerParameter

• required float spatial_scale = 1

• required int32 output_dim = 2

  output channel number

• required int32 group_size = 3

  number of groups to encode position-sensitive score maps

Specifies training parameters (multipliers on global learning constants, and the name and other settings used for weight sharing).

Used in: LayerParameter

• optional string name = 1

  The names of the parameter blobs -- useful for sharing parameters among layers, but never required otherwise. To share a parameter between two layers, give it a (non-empty) name.

• optional ParamSpec.DimCheckMode share_mode = 2

  Whether to require shared weights to have the same shape, or just the same count -- defaults to STRICT if unspecified.

• optional float lr_mult = 3

  The multiplier on the global learning rate for this parameter.

• optional float decay_mult = 4

  The multiplier on the global weight decay for this parameter.

Used in: ParamSpec

• STRICT = 0

  STRICT (default) requires that num, channels, height, width each match.

• PERMISSIVE = 1

  PERMISSIVE requires only the count (num*channels*height*width) to match.

Used in: LayerParameter

• optional BlobShape shape = 1

Used in: LayerParameter, NetState, NetStateRule

• TRAIN = 0

• TEST = 1

Used in: LayerParameter, V1LayerParameter

• optional PoolingParameter.PoolMethod pool = 1

  The pooling method

• optional uint32 pad = 4

  Pad, kernel size, and stride are all given as a single value for equal dimensions in height and width or as Y, X pairs.

  The padding size (equal in Y, X)

• optional uint32 pad_h = 9

  The padding height

• optional uint32 pad_w = 10

  The padding width

• optional uint32 kernel_size = 2

  The kernel size (square)

• optional uint32 kernel_h = 5

  The kernel height

• optional uint32 kernel_w = 6

  The kernel width

• optional uint32 stride = 3

  The stride (equal in Y, X)

• optional uint32 stride_h = 7

  The stride height

• optional uint32 stride_w = 8

  The stride width

• optional PoolingParameter.Engine engine = 11

• optional bool global_pooling = 12

  If global_pooling then it will pool over the size of the bottom by doing kernel_h = bottom->height and kernel_w = bottom->width

Used in: PoolingParameter

• DEFAULT = 0

• CAFFE = 1

• CUDNN = 2

Used in: PoolingParameter

• MAX = 0

• AVE = 1

• STOCHASTIC = 2

Used in: LayerParameter, V1LayerParameter

• optional float power = 1

  PowerLayer computes outputs y = (shift + scale * x) ^ power.

• optional float scale = 2

• optional float shift = 3

Used in: LayerParameter

• optional string module = 1

• optional string layer = 2

• optional string param_str = 3

  This value is set to the attribute `param_str` of the `PythonLayer` object in Python before calling the `setup()` method. This could be a number, string, dictionary in Python dict format, JSON, etc. You may parse this string in `setup` method and use it in `forward` and `backward`.

• optional bool share_in_parallel = 4

  Whether this PythonLayer is shared among worker solvers during data parallelism. If true, each worker solver sequentially run forward from this layer. This value should be set true if you are using it as a data layer.

Message that stores parameters used by ROIPoolingLayer

Used in: LayerParameter

• optional uint32 pooled_h = 1

  Pad, kernel size, and stride are all given as a single value for equal dimensions in height and width or as Y, X pairs.

  The pooled output height

• optional uint32 pooled_w = 2

  The pooled output width

• optional float spatial_scale = 3

  Multiplicative spatial scale factor to translate ROI coords from their input scale to the scale used when pooling

Message that stores parameters used by ReLULayer

Used in: LayerParameter, V1LayerParameter

• optional float negative_slope = 1

  Allow non-zero slope for negative inputs to speed up optimization Described in: Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities improve neural network acoustic models. In ICML Workshop on Deep Learning for Audio, Speech, and Language Processing.

• optional ReLUParameter.Engine engine = 2

Used in: ReLUParameter

• DEFAULT = 0

• CAFFE = 1

• CUDNN = 2

Message that stores parameters used by RecurrentLayer

Used in: LayerParameter

• optional uint32 num_output = 1

  The dimension of the output (and usually hidden state) representation -- must be explicitly set to non-zero.

• optional FillerParameter weight_filler = 2

  The filler for the weight

• optional FillerParameter bias_filler = 3

  The filler for the bias

• optional bool debug_info = 4

  Whether to enable displaying debug_info in the unrolled recurrent net.

• optional bool expose_hidden = 5

  Whether to add as additional inputs (bottoms) the initial hidden state blobs, and add as additional outputs (tops) the final timestep hidden state blobs. The number of additional bottom/top blobs required depends on the recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.

Message that stores parameters used by ReductionLayer

Used in: LayerParameter

• optional ReductionParameter.ReductionOp operation = 1

  reduction operation

• optional int32 axis = 2

  The first axis to reduce to a scalar -- may be negative to index from the end (e.g., -1 for the last axis). (Currently, only reduction along ALL "tail" axes is supported; reduction of axis M through N, where N < num_axes - 1, is unsupported.) Suppose we have an n-axis bottom Blob with shape: (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)). If axis == m, the output Blob will have shape (d0, d1, d2, ..., d(m-1)), and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1)) times, each including (dm * d(m+1) * ... * d(n-1)) individual data. If axis == 0 (the default), the output Blob always has the empty shape (count 1), performing reduction across the entire input -- often useful for creating new loss functions.

• optional float coeff = 3

  coefficient for output

Used in: ReductionParameter

• SUM = 1

• ASUM = 2

• SUMSQ = 3

• MEAN = 4

Used in: LayerParameter

• optional BlobShape shape = 1

  Specify the output dimensions. If some of the dimensions are set to 0, the corresponding dimension from the bottom layer is used (unchanged). Exactly one dimension may be set to -1, in which case its value is inferred from the count of the bottom blob and the remaining dimensions. For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8: layer { type: "Reshape" bottom: "input" top: "output" reshape_param { ... } } If "input" is 2D with shape 2 x 8, then the following reshape_param specifications are all equivalent, producing a 3D blob "output" with shape 2 x 2 x 4: reshape_param { shape { dim: 2 dim: 2 dim: 4 } } reshape_param { shape { dim: 0 dim: 2 dim: 4 } } reshape_param { shape { dim: 0 dim: 2 dim: -1 } } reshape_param { shape { dim: 0 dim:-1 dim: 4 } }

• optional int32 axis = 2

  axis and num_axes control the portion of the bottom blob's shape that are replaced by (included in) the reshape. By default (axis == 0 and num_axes == -1), the entire bottom blob shape is included in the reshape, and hence the shape field must specify the entire output shape. axis may be non-zero to retain some portion of the beginning of the input shape (and may be negative to index from the end; e.g., -1 to begin the reshape after the last axis, including nothing in the reshape, -2 to include only the last axis, etc.). For example, suppose "input" is a 2D blob with shape 2 x 8. Then the following ReshapeLayer specifications are all equivalent, producing a blob "output" with shape 2 x 2 x 4: reshape_param { shape { dim: 2 dim: 2 dim: 4 } } reshape_param { shape { dim: 2 dim: 4 } axis: 1 } reshape_param { shape { dim: 2 dim: 4 } axis: -3 } num_axes specifies the extent of the reshape. If num_axes >= 0 (and axis >= 0), the reshape will be performed only on input axes in the range [axis, axis+num_axes]. num_axes may also be -1, the default, to include all remaining axes (starting from axis). For example, suppose "input" is a 2D blob with shape 2 x 8. Then the following ReshapeLayer specifications are equivalent, producing a blob "output" with shape 1 x 2 x 8. reshape_param { shape { dim: 1 dim: 2 dim: 8 } } reshape_param { shape { dim: 1 dim: 2 } num_axes: 1 } reshape_param { shape { dim: 1 } num_axes: 0 } On the other hand, these would produce output blob shape 2 x 1 x 8: reshape_param { shape { dim: 2 dim: 1 dim: 8 } } reshape_param { shape { dim: 1 } axis: 1 num_axes: 0 }

• optional int32 num_axes = 3

Used in: LayerParameter

• optional uint32 pyramid_height = 1

• optional SPPParameter.PoolMethod pool = 2

  The pooling method

• optional SPPParameter.Engine engine = 6

Used in: SPPParameter

• DEFAULT = 0

• CAFFE = 1

• CUDNN = 2

Used in: SPPParameter

• MAX = 0

• AVE = 1

• STOCHASTIC = 2

Used in: LayerParameter

• optional int32 axis = 1

  The first axis of bottom[0] (the first input Blob) along which to apply bottom[1] (the second input Blob). May be negative to index from the end (e.g., -1 for the last axis). For example, if bottom[0] is 4D with shape 100x3x40x60, the output top[0] will have the same shape, and bottom[1] may have any of the following shapes (for the given value of axis): (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60 (axis == 1 == -3) 3; 3x40; 3x40x60 (axis == 2 == -2) 40; 40x60 (axis == 3 == -1) 60 Furthermore, bottom[1] may have the empty shape (regardless of the value of "axis") -- a scalar multiplier.

• optional int32 num_axes = 2

  (num_axes is ignored unless just one bottom is given and the scale is a learned parameter of the layer. Otherwise, num_axes is determined by the number of axes by the second bottom.) The number of axes of the input (bottom[0]) covered by the scale parameter, or -1 to cover all axes of bottom[0] starting from `axis`. Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.

• optional FillerParameter filler = 3

  (filler is ignored unless just one bottom is given and the scale is a learned parameter of the layer.) The initialization for the learned scale parameter. Default is the unit (1) initialization, resulting in the ScaleLayer initially performing the identity operation.

• optional bool bias_term = 4

  Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but may be more efficient). Initialized with bias_filler (defaults to 0).

• optional FillerParameter bias_filler = 5

Used in: LayerParameter, V1LayerParameter

• optional SigmoidParameter.Engine engine = 1

Used in: SigmoidParameter

• DEFAULT = 0

• CAFFE = 1

• CUDNN = 2

Used in: LayerParameter, V1LayerParameter

• optional int32 axis = 3

  The axis along which to slice -- may be negative to index from the end (e.g., -1 for the last axis). By default, SliceLayer concatenates blobs along the "channels" axis (1).

• repeated uint32 slice_point = 2

• optional uint32 slice_dim = 1

  DEPRECATED: alias for "axis" -- does not support negative indexing.

Used in: LayerParameter

• optional float sigma = 1

  SmoothL1Loss(x) = 0.5 * (sigma * x) ** 2 -- if x < 1.0 / sigma / sigma |x| - 0.5 / sigma / sigma -- otherwise

Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer

Used in: LayerParameter, V1LayerParameter

• optional SoftmaxParameter.Engine engine = 1

• optional int32 axis = 2

  The axis along which to perform the softmax -- may be negative to index from the end (e.g., -1 for the last axis). Any other axes will be evaluated as independent softmaxes.

Used in: SoftmaxParameter

• DEFAULT = 0

• CAFFE = 1

• CUDNN = 2

NOTE Update the next available ID when you add a new SolverParameter field. SolverParameter next available ID: 42 (last added: layer_wise_reduce)

//////////////////////////////////////////////////////////////////////////// Specifying the train and test networks Exactly one train net must be specified using one of the following fields: train_net_param, train_net, net_param, net One or more test nets may be specified using any of the following fields: test_net_param, test_net, net_param, net If more than one test net field is specified (e.g., both net and test_net are specified), they will be evaluated in the field order given above: (1) test_net_param, (2) test_net, (3) net_param/net. A test_iter must be specified for each test_net. A test_level and/or a test_stage may also be specified for each test_net. ////////////////////////////////////////////////////////////////////////////

• optional string net = 24

  Proto filename for the train net, possibly combined with one or more test nets.

• optional NetParameter net_param = 25

  Inline train net param, possibly combined with one or more test nets.

• optional string train_net = 1

  Proto filename for the train net.

• repeated string test_net = 2

  Proto filenames for the test nets.

• optional NetParameter train_net_param = 21

  Inline train net params.

• repeated NetParameter test_net_param = 22

  Inline test net params.

• optional NetState train_state = 26

  The states for the train/test nets. Must be unspecified or specified once per net. By default, all states will have solver = true; train_state will have phase = TRAIN, and all test_state's will have phase = TEST. Other defaults are set according to the NetState defaults.

• repeated NetState test_state = 27

• repeated int32 test_iter = 3

  The number of iterations for each test net.

• optional int32 test_interval = 4

  The number of iterations between two testing phases.

• optional bool test_compute_loss = 19

• optional bool test_initialization = 32

  If true, run an initial test pass before the first iteration, ensuring memory availability and printing the starting value of the loss.

• optional float base_lr = 5

  The base learning rate

• optional int32 display = 6

  the number of iterations between displaying info. If display = 0, no info will be displayed.

• optional int32 average_loss = 33

  Display the loss averaged over the last average_loss iterations

• optional int32 max_iter = 7

  the maximum number of iterations

• optional int32 iter_size = 36

  accumulate gradients over `iter_size` x `batch_size` instances

• optional string lr_policy = 8

  The learning rate decay policy. The currently implemented learning rate policies are as follows: - fixed: always return base_lr. - step: return base_lr * gamma ^ (floor(iter / step)) - exp: return base_lr * gamma ^ iter - inv: return base_lr * (1 + gamma * iter) ^ (- power) - multistep: similar to step but it allows non uniform steps defined by stepvalue - poly: the effective learning rate follows a polynomial decay, to be zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power) - sigmoid: the effective learning rate follows a sigmod decay return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize)))) where base_lr, max_iter, gamma, step, stepvalue and power are defined in the solver parameter protocol buffer, and iter is the current iteration.

• optional float gamma = 9

  The parameter to compute the learning rate.

• optional float power = 10

  The parameter to compute the learning rate.

• optional float momentum = 11

  The momentum value.

• optional float weight_decay = 12

  The weight decay.

• optional string regularization_type = 29

  regularization types supported: L1 and L2 controlled by weight_decay

• optional int32 stepsize = 13

  the stepsize for learning rate policy "step"

• repeated int32 stepvalue = 34

  the stepsize for learning rate policy "multistep"

• optional float clip_gradients = 35

  Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm, whenever their actual L2 norm is larger.

• optional int32 snapshot = 14

  The snapshot interval

• optional string snapshot_prefix = 15

  The prefix for the snapshot.

• optional bool snapshot_diff = 16

  whether to snapshot diff in the results or not. Snapshotting diff will help debugging but the final protocol buffer size will be much larger.

• optional SolverParameter.SnapshotFormat snapshot_format = 37

• optional SolverParameter.SolverMode solver_mode = 17

• optional int32 device_id = 18

  the device_id will that be used in GPU mode. Use device_id = 0 in default.

• optional int64 random_seed = 20

  If non-negative, the seed with which the Solver will initialize the Caffe random number generator -- useful for reproducible results. Otherwise, (and by default) initialize using a seed derived from the system clock.

• optional string type = 40

  type of the solver

• optional float delta = 31

  numerical stability for RMSProp, AdaGrad and AdaDelta and Adam

• optional float momentum2 = 39

  parameters for the Adam solver

• optional float rms_decay = 38

  RMSProp decay value MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)

• optional bool debug_info = 23

  If true, print information about the state of the net that may help with debugging learning problems.

• optional bool snapshot_after_train = 28

  If false, don't save a snapshot after training finishes.

• optional SolverParameter.SolverType solver_type = 30

  DEPRECATED: use type instead of solver_type

• optional bool layer_wise_reduce = 41

  Overlap compute and communication for data parallel training

Used in: SolverParameter

• HDF5 = 0

• BINARYPROTO = 1

the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.

Used in: SolverParameter

• CPU = 0

• GPU = 1

DEPRECATED: old solver enum types, use string instead

Used in: SolverParameter

• SGD = 0

• NESTEROV = 1

• ADAGRAD = 2

• RMSPROP = 3

• ADADELTA = 4

• ADAM = 5

A message that stores the solver snapshots

• optional int32 iter = 1

  The current iteration

• optional string learned_net = 2

  The file that stores the learned net.

• repeated BlobProto history = 3

  The history for sgd solvers

• optional int32 current_step = 4

  The current step for learning rate

Used in: LayerParameter, V1LayerParameter

• optional TanHParameter.Engine engine = 1

Used in: TanHParameter

• DEFAULT = 0

• CAFFE = 1

• CUDNN = 2

Message that stores parameters used by ThresholdLayer

Used in: LayerParameter, V1LayerParameter

• optional float threshold = 1

  Strictly positive values

Message that stores parameters used by TileLayer

Used in: LayerParameter

• optional int32 axis = 1

  The index of the axis to tile.

• optional int32 tiles = 2

  The number of copies (tiles) of the blob to output.

Message that stores parameters used to apply transformation to the data layer's data

Used in: LayerParameter, V1LayerParameter

• optional float scale = 1

  For data pre-processing, we can do simple scaling and subtracting the data mean, if provided. Note that the mean subtraction is always carried out before scaling.

• optional bool mirror = 2

  Specify if we want to randomly mirror data.

• optional uint32 crop_size = 3

  Specify if we would like to randomly crop an image.

• optional string mean_file = 4

  mean_file and mean_value cannot be specified at the same time

• repeated float mean_value = 5

  if specified can be repeated once (would subtract it from all the channels) or can be repeated the same number of times as channels (would subtract them from the corresponding channel)

• optional bool force_color = 6

  Force the decoded image to have 3 color channels.

• optional bool force_gray = 7

  Force the decoded image to have 1 color channels.

DEPRECATED: V0LayerParameter is the old way of specifying layer parameters in Caffe. We keep this message type around for legacy support.

Used in: V1LayerParameter

• optional string name = 1

  the layer name

• optional string type = 2

  the string to specify the layer type

• optional uint32 num_output = 3

  Parameters to specify layers with inner products.

  The number of outputs for the layer

• optional bool biasterm = 4

  whether to have bias terms

• optional FillerParameter weight_filler = 5

  The filler for the weight

• optional FillerParameter bias_filler = 6

  The filler for the bias

• optional uint32 pad = 7

  The padding size

• optional uint32 kernelsize = 8

  The kernel size

• optional uint32 group = 9

  The group size for group conv

• optional uint32 stride = 10

  The stride

• optional V0LayerParameter.PoolMethod pool = 11

  The pooling method

• optional float dropout_ratio = 12

  dropout ratio

• optional uint32 local_size = 13

  for local response norm

• optional float alpha = 14

  for local response norm

• optional float beta = 15

  for local response norm

• optional float k = 22

• optional string source = 16

  For data layers, specify the data source

• optional float scale = 17

  For data pre-processing, we can do simple scaling and subtracting the data mean, if provided. Note that the mean subtraction is always carried out before scaling.

• optional string meanfile = 18

• optional uint32 batchsize = 19

  For data layers, specify the batch size.

• optional uint32 cropsize = 20

  For data layers, specify if we would like to randomly crop an image.

• optional bool mirror = 21

  For data layers, specify if we want to randomly mirror data.

• repeated BlobProto blobs = 50

  The blobs containing the numeric parameters of the layer

• repeated float blobs_lr = 51

  The ratio that is multiplied on the global learning rate. If you want to set the learning ratio for one blob, you need to set it for all blobs.

• repeated float weight_decay = 52

  The weight decay that is multiplied on the global weight decay.

• optional uint32 rand_skip = 53

  The rand_skip variable is for the data layer to skip a few data points to avoid all asynchronous sgd clients to start at the same point. The skip point would be set as rand_skip * rand(0,1). Note that rand_skip should not be larger than the number of keys in the database.

• optional float det_fg_threshold = 54

  Fields related to detection (det_*) foreground (object) overlap threshold

• optional float det_bg_threshold = 55

  background (non-object) overlap threshold

• optional float det_fg_fraction = 56

  Fraction of batch that should be foreground objects

• optional uint32 det_context_pad = 58

  Amount of contextual padding to add around a window (used only by the window_data_layer)

• optional string det_crop_mode = 59

  Mode for cropping out a detection window warp: cropped window is warped to a fixed size and aspect ratio square: the tightest square around the window is cropped

• optional int32 new_num = 60

  For ReshapeLayer, one needs to specify the new dimensions.

• optional int32 new_channels = 61

• optional int32 new_height = 62

• optional int32 new_width = 63

• optional bool shuffle_images = 64

  Whether or not ImageLayer should shuffle the list of files at every epoch. It will also resize images if new_height or new_width are not zero.

• optional uint32 concat_dim = 65

  For ConcatLayer, one needs to specify the dimension for concatenation, and the other dimensions must be the same for all the bottom blobs. By default it will concatenate blobs along the channels dimension.

• optional HDF5OutputParameter hdf5_output_param = 1001

Used in: V0LayerParameter

• MAX = 0

• AVE = 1

• STOCHASTIC = 2

DEPRECATED: use LayerParameter.

Used in: NetParameter

• repeated string bottom = 2

• repeated string top = 3

• optional string name = 4

• repeated NetStateRule include = 32

• repeated NetStateRule exclude = 33

• optional V1LayerParameter.LayerType type = 5

• repeated BlobProto blobs = 6

• repeated string param = 1001

• repeated V1LayerParameter.DimCheckMode blob_share_mode = 1002

• repeated float blobs_lr = 7

• repeated float weight_decay = 8

• repeated float loss_weight = 35

• optional AccuracyParameter accuracy_param = 27

• optional ArgMaxParameter argmax_param = 23

• optional ConcatParameter concat_param = 9

• optional ContrastiveLossParameter contrastive_loss_param = 40

• optional ConvolutionParameter convolution_param = 10

• optional DataParameter data_param = 11

• optional DropoutParameter dropout_param = 12

• optional DummyDataParameter dummy_data_param = 26

• optional EltwiseParameter eltwise_param = 24

• optional ExpParameter exp_param = 41

• optional HDF5DataParameter hdf5_data_param = 13

• optional HDF5OutputParameter hdf5_output_param = 14

• optional HingeLossParameter hinge_loss_param = 29

• optional ImageDataParameter image_data_param = 15

• optional InfogainLossParameter infogain_loss_param = 16

• optional InnerProductParameter inner_product_param = 17

• optional LRNParameter lrn_param = 18

• optional MemoryDataParameter memory_data_param = 22

• optional MVNParameter mvn_param = 34

• optional PoolingParameter pooling_param = 19

• optional PowerParameter power_param = 21

• optional ReLUParameter relu_param = 30

• optional SigmoidParameter sigmoid_param = 38

• optional SoftmaxParameter softmax_param = 39

• optional SliceParameter slice_param = 31

• optional TanHParameter tanh_param = 37

• optional ThresholdParameter threshold_param = 25

• optional WindowDataParameter window_data_param = 20

• optional TransformationParameter transform_param = 36

• optional LossParameter loss_param = 42

• optional V0LayerParameter layer = 1

Used in: V1LayerParameter

• STRICT = 0

• PERMISSIVE = 1

Used in: V1LayerParameter

• NONE = 0

• ABSVAL = 35

• ACCURACY = 1

• ARGMAX = 30

• BNLL = 2

• CONCAT = 3

• CONTRASTIVE_LOSS = 37

• CONVOLUTION = 4

• DATA = 5

• DECONVOLUTION = 39

• DROPOUT = 6

• DUMMY_DATA = 32

• EUCLIDEAN_LOSS = 7

• ELTWISE = 25

• EXP = 38

• FLATTEN = 8

• HDF5_DATA = 9

• HDF5_OUTPUT = 10

• HINGE_LOSS = 28

• IM2COL = 11

• IMAGE_DATA = 12

• INFOGAIN_LOSS = 13

• INNER_PRODUCT = 14

• LRN = 15

• MEMORY_DATA = 29

• MULTINOMIAL_LOGISTIC_LOSS = 16

• MVN = 34

• POOLING = 17

• POWER = 26

• RELU = 18

• SIGMOID = 19

• SIGMOID_CROSS_ENTROPY_LOSS = 27

• SILENCE = 36

• SOFTMAX = 20

• SOFTMAX_LOSS = 21

• SPLIT = 22

• SLICE = 33

• TANH = 23

• WINDOW_DATA = 24

• THRESHOLD = 31

Used in: LayerParameter, V1LayerParameter

• optional string source = 1

  Specify the data source.

• optional float scale = 2

  For data pre-processing, we can do simple scaling and subtracting the data mean, if provided. Note that the mean subtraction is always carried out before scaling.

• optional string mean_file = 3

• optional uint32 batch_size = 4

  Specify the batch size.

• optional uint32 crop_size = 5

  Specify if we would like to randomly crop an image.

• optional bool mirror = 6

  Specify if we want to randomly mirror data.

• optional float fg_threshold = 7

  Foreground (object) overlap threshold

• optional float bg_threshold = 8

  Background (non-object) overlap threshold

• optional float fg_fraction = 9

  Fraction of batch that should be foreground objects

• optional uint32 context_pad = 10

  Amount of contextual padding to add around a window (used only by the window_data_layer)

• optional string crop_mode = 11

  Mode for cropping out a detection window warp: cropped window is warped to a fixed size and aspect ratio square: the tightest square around the window is cropped

• optional bool cache_images = 12

  cache_images: will load all images in memory for faster access

• optional string root_folder = 13

  append root_folder to locate images