package caffe
Get desktop application:
View/edit binary Protocol Buffers messages
message
caffe.proto:371Message that stores parameters used by AccuracyLayer
Used in:
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).
message
caffe.proto:379Message that stores parameters used by ArgMaxLayer
Used in:
optional bool out_max_val = 1
If true produce pairs (argmax, maxval)
optional uint32 top_k = 2
message
caffe.proto:5Used in: , , ,
optional int32 num = 1
optional int32 channels = 2
optional int32 height = 3
optional int32 width = 4
repeated float data = 5
repeated float diff = 6
message
caffe.proto:16The BlobProtoVector is simply a way to pass multiple blobproto instances around.
repeated blobs = 1
message
caffe.proto:386Message that stores parameters used by ConcatLayer
Used in:
optional uint32 concat_dim = 1
Concat Layer needs to specify the dimension along the concat will happen, the other dimensions must be the same for all the bottom blobs By default it will concatenate blobs along channels dimension
message
caffe.proto:394Message that stores parameters used by ContrastiveLossLayer
Used in:
optional float margin = 1
margin for dissimilar pair
message
caffe.proto:400Message that stores parameters used by ConvolutionLayer
Used in:
optional uint32 num_output = 1
The number of outputs for the layer
optional bool bias_term = 2
whether to have bias terms
optional uint32 pad = 3
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 = 4
The kernel size (square)
optional uint32 kernel_h = 11
The kernel height
optional uint32 kernel_w = 12
The kernel width
optional uint32 group = 5
The group size for group conv
optional uint32 stride = 6
The stride (equal in Y, X)
optional uint32 stride_h = 13
The stride height
optional uint32 stride_w = 14
The stride width
optional weight_filler = 7
The filler for the weight
optional bias_filler = 8
The filler for the bias
optional ConvolutionParameter.Engine engine = 15
Used in:
DEFAULT = 0
CAFFE = 1
CUDNN = 2
message
caffe.proto:426Message that stores parameters used by DataLayer
Used in:
optional string source = 1
Specify the data source.
optional string mem_data_source = 16
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 leveldb.
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 uint32 mpi_skip_step = 15
Used in:
LEVELDB = 0
LMDB = 1
message
caffe.proto:20optional int32 channels = 1
optional int32 height = 2
optional int32 width = 3
optional bytes data = 4
the actual image data, in bytes
repeated float label = 5
repeated float float_data = 6
Optionally, the datum could also hold float data.
message
caffe.proto:457Message that stores parameters used by DropoutLayer
Used in:
optional float dropout_ratio = 1
dropout ratio
message
caffe.proto:464Message that stores parameters used by DummyDataLayer. DummyDataLayer fills any number of arbitrarily shaped blobs with random (or constant) data generated by "Fillers" (see "message FillerParameter").
Used in:
repeated data_filler = 1
This layer produces N >= 1 top blobs. DummyDataParameter must specify 1 or N num, N channels, N height, and N width fields, and must specify 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 uint32 num = 2
repeated uint32 channels = 3
repeated uint32 height = 4
repeated uint32 width = 5
message
caffe.proto:480Message that stores parameters used by EltwiseLayer
Used in:
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:
PROD = 0
SUM = 1
MAX = 2
message
caffe.proto:31Used in: , , ,
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 input weights for a given output in Gaussian filler -- the default -1 means don't perform sparsification.
message
caffe.proto:564Message that stores paramters used by GradientScalerLayer
Used in:
required float lower_bound = 1
required float upper_bound = 2
required float alpha = 3
required float max_iter = 4
message
caffe.proto:500Message that stores parameters used by HDF5DataLayer
Used in:
optional string source = 1
Specify the data source.
optional uint32 batch_size = 2
Specify the batch size.
message
caffe.proto:508Message that stores parameters used by HDF5OutputLayer
Used in: ,
optional string file_name = 1
message
caffe.proto:512Used in:
optional HingeLossParameter.Norm norm = 1
Specify the Norm to use L1 or L2
Used in:
L1 = 1
L2 = 2
message
caffe.proto:522Message that stores parameters used by ImageDataLayer
Used in:
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 leveldb.
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 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.
message
caffe.proto:551Message that stores parameters InfogainLossLayer
Used in:
optional string source = 1
Specify the infogain matrix source.
message
caffe.proto:557Message that stores parameters used by InnerProductLayer
Used in:
optional uint32 num_output = 1
The number of outputs for the layer
optional bool bias_term = 2
whether to have bias terms
optional weight_filler = 3
The filler for the weight
optional bias_filler = 4
The filler for the bias
message
caffe.proto:584Message that stores parameters used by LRNLayer
Used in:
optional uint32 local_size = 1
optional float alpha = 2
optional float beta = 3
optional LRNParameter.NormRegion norm_region = 4
Used in:
ACROSS_CHANNELS = 0
WITHIN_CHANNEL = 1
message
caffe.proto:572Massage that stors paramters used by TransformSoftmaxWithLoss
Used in:
required string label_transform_file = 1
message
caffe.proto:205NOTE Update the next available ID when you add a new LayerParameter field. LayerParameter next available ID: 42 (last added: label_transform_param)
Used in: ,
repeated string bottom = 2
the name of the bottom blobs
repeated string top = 3
the name of the top blobs
optional string name = 4
the layer name
repeated include = 32
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 exclude = 33
optional LayerParameter.LayerType type = 5
the layer type from the enum above
optional int32 label_size = 50
The label size for data layer
repeated blobs = 6
The blobs containing the numeric parameters of the layer
repeated string param = 1001
The names of the parameter blobs -- useful for sharing parameters among layers (but never required).
repeated LayerParameter.DimCheckMode blob_share_mode = 1002
Whether to require shared weights to have the same shape, or just the same count -- defaults to STRICT if unspecified.
repeated float blobs_lr = 7
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 = 8
The weight decay that is multiplied on the global weight decay.
repeated float loss_weight = 35
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.
optional accuracy_param = 27
optional argmax_param = 23
optional concat_param = 9
optional contrastive_loss_param = 40
optional convolution_param = 10
optional data_param = 11
optional dropout_param = 12
optional dummy_data_param = 26
optional eltwise_param = 24
optional gradient_scaler_param = 43
optional hdf5_data_param = 13
optional hdf5_output_param = 14
optional hinge_loss_param = 29
optional image_data_param = 15
optional infogain_loss_param = 16
optional inner_product_param = 17
optional label_transform_param = 41
optional tree_param = 42
optional lrn_param = 18
optional memory_data_param = 22
optional mvn_param = 34
optional pooling_param = 19
optional power_param = 21
optional relu_param = 30
optional sigmoid_param = 38
optional softmax_param = 39
optional slice_param = 31
optional tanh_param = 37
optional threshold_param = 25
optional window_data_param = 20
optional transform_param = 36
Parameters for data pre-processing.
optional layer = 1
DEPRECATED: The layer parameters specified as a V0LayerParameter. This should never be used by any code except to upgrade to the new LayerParameter specification.
Used in:
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.
NOTE Add new LayerTypes to the enum below in lexicographical order (other than starting with NONE), starting with the next available ID in the comment line above the enum. Update the next available ID when you add a new LayerType. LayerType next available ID: 56 (last added: WEIGHTED_EUCLIEAN_LOSS)
Used in:
NONE = 0
"NONE" layer type is 0th enum element so that we don't cause confusion by defaulting to an existent LayerType (instead, should usually error if the type is unspecified).
ABSVAL = 35
ACCURACY = 1
ARGMAX = 30
BNLL = 2
COMPACT_DATA = 43
COMPACT_DATA_BBOX = 52
CONCAT = 3
CONTRASTIVE_LOSS = 37
CONVOLUTION = 4
DATA = 5
DROPOUT = 6
DUMMY_DATA = 32
EUCLIDEAN_LOSS = 7
ELTWISE = 25
EXT_LABEL_ACCURACY = 51
FLATTEN = 8
GRADIENT_SCALER = 55
HDF5_DATA = 9
HDF5_OUTPUT = 10
HINGE_LOSS = 28
IM2COL = 11
IMAGE_DATA = 12
INFOGAIN_LOSS = 13
INNER_PRODUCT = 14
LABEL_TRANSFORM = 38
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
SOFTMAX_LOSS_WITH_INVALID = 53
SOFT_LOGISTIC_LOSS = 50
SPLIT = 22
SLICE = 33
TANH = 23
TRANSFORM_SOFTMAX_LOSS = 39
SOFTMAX_LOSS_TREE = 42
ACCURACY_TREE = 41
TRANSFORM_ACCURACY = 40
WINDOW_DATA = 24
THRESHOLD = 31
WEIGHTED_EUCLIDEAN_LOSS = 56
message
caffe.proto:604Message that stores parameters used by MVNLayer
Used in:
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
message
caffe.proto:596Message that stores parameters used by MemoryDataLayer
Used in:
optional uint32 batch_size = 1
optional uint32 channels = 2
optional uint32 height = 3
optional uint32 width = 4
message
caffe.proto:44Used in:
optional string name = 1
consider giving the network a name
repeated layers = 2
a bunch of layers.
repeated string input = 3
The input blobs to the network.
repeated int32 input_dim = 4
The dim of the input blobs. 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 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.
message
caffe_pretty_print.proto:12A near-duplicate of NetParameter with fields re-numbered to beautify automatic prototext dumps. The main practical purpose is to print inputs before layers, because having inputs at the end looks weird. NetParameterPrettyPrint should never be used in code except for conversion FROM NetParameter and subsequent dumping to proto text file.
optional string name = 1
optional bool force_backward = 2
repeated string input = 3
repeated int32 input_dim = 4
repeated layers = 5
message
caffe.proto:177Used in: ,
optional Phase phase = 1
optional int32 level = 2
repeated string stage = 3
message
caffe.proto:183Used in:
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
enum Phase
caffe.proto:172Used in: ,
TRAIN = 0
TEST = 1
message
caffe.proto:613Message that stores parameters used by PoolingLayer
Used in:
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
Used in:
DEFAULT = 0
CAFFE = 1
CUDNN = 2
Used in:
MAX = 0
AVE = 1
STOCHASTIC = 2
message
caffe.proto:640Message that stores parameters used by PowerLayer
Used in:
optional float power = 1
PowerLayer computes outputs y = (shift + scale * x) ^ power.
optional float scale = 2
optional float shift = 3
message
caffe.proto:648Message that stores parameters used by ReLULayer
Used in:
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:
DEFAULT = 0
CAFFE = 1
CUDNN = 2
message
caffe.proto:664Message that stores parameters used by SigmoidLayer
Used in:
optional SigmoidParameter.Engine engine = 1
Used in:
DEFAULT = 0
CAFFE = 1
CUDNN = 2
message
caffe.proto:674Message that stores parameters used by SliceLayer
Used in:
optional uint32 slice_dim = 1
SliceLayer needs to know which dimension to slice across. Currently, SliceLayer only supports slicing across num (dim 0) and channels (dim 1). By default, SliceLayer slices across channels.
repeated uint32 slice_point = 2
message
caffe.proto:684Message that stores parameters used by SoftmaxLayer, SoftMaxWithLossLayer
Used in:
optional SoftmaxParameter.Engine engine = 1
Used in:
DEFAULT = 0
CAFFE = 1
CUDNN = 2
message
caffe.proto:67NOTE Update the next available ID when you add a new SolverParameter field. SolverParameter next available ID: 33 (last added: test_initialization)
//////////////////////////////////////////////////////////////////////////// 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 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 train_net_param = 21
Inline train net params.
repeated test_net_param = 22
Inline test net params.
optional 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 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 max_iter = 7
the maximum number of iterations
optional string lr_policy = 8
The learning rate decay policy.
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"
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.SolverMode solver_mode = 17
repeated 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 int32 update_interval = 33
added to allow big batch_size
optional SolverParameter.SolverType solver_type = 30
optional float delta = 31
numerical stability for AdaGrad
optional bool debug_info = 23
If true, print information about the state of the net that may help with debugging learning problems.
optional int32 debug_display = 40
optional bool snapshot_after_train = 28
If false, don't save a snapshot after training finishes.
the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.
Used in:
CPU = 0
GPU = 1
Solver type
Used in:
SGD = 0
NESTEROV = 1
ADAGRAD = 2
message
caffe.proto:166A 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 history = 3
The history for sgd solvers
message
caffe.proto:694Message that stores parameters used by SigmoidLayer
Used in:
optional TanHParameter.Engine engine = 1
Used in:
DEFAULT = 0
CAFFE = 1
CUDNN = 2
message
caffe.proto:495Message that stores parameters used by ThresholdLayer
Used in:
optional float threshold = 1
Strictly Positive values
message
caffe.proto:357Message that stores parameters used to apply transformation to the data layer's data
Used in:
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
optional bool multiscale = 5
message
caffe.proto:577Massage that stors paramters used by TreeLayers
Used in:
repeated float node_weight = 1
required uint32 tree_depth = 2
repeated string label_transform_file = 3
message
caffe.proto:736DEPRECATED: V0LayerParameter is the old way of specifying layer parameters in Caffe. We keep this message type around for legacy support.
Used in:
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 weight_filler = 5
The filler for the weight
optional 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 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 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 leveldb.
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 hdf5_output_param = 1001
Used in:
MAX = 0
AVE = 1
STOCHASTIC = 2
message
caffe.proto:704Message that stores parameters used by WindowDataLayer
Used in:
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 shuffle = 12