Deep learning framework by BAIR
Created by Yangqing Jia Lead Developer Evan Shelhamer
WindowData
./include/caffe/layers/window_data_layer.hpp
./src/caffe/layers/window_data_layer.cpp
WindowDataParameter
./src/caffe/proto/caffe.proto
message WindowDataParameter { // Specify the data source. optional string source = 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 float scale = 2 [default = 1]; optional string mean_file = 3; // Specify the batch size. optional uint32 batch_size = 4; // Specify if we would like to randomly crop an image. optional uint32 crop_size = 5 [default = 0]; // Specify if we want to randomly mirror data. optional bool mirror = 6 [default = false]; // Foreground (object) overlap threshold optional float fg_threshold = 7 [default = 0.5]; // Background (non-object) overlap threshold optional float bg_threshold = 8 [default = 0.5]; // Fraction of batch that should be foreground objects optional float fg_fraction = 9 [default = 0.25]; // Amount of contextual padding to add around a window // (used only by the window_data_layer) optional uint32 context_pad = 10 [default = 0]; // 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 string crop_mode = 11 [default = "warp"]; // cache_images: will load all images in memory for faster access optional bool cache_images = 12 [default = false]; // append root_folder to locate images optional string root_folder = 13 [default = ""]; }