yigitcolakoglu
/
MyCity


								syntax = "proto2";


								package object_detection.protos;


								// Configuration proto for defining input readers that generate Object Detection

								// Examples from input sources. Input readers are expected to generate a

								// dictionary of tensors, with the following fields populated:

								//

								// 'image': an [image_height, image_width, channels] image tensor that detection

								//    will be run on.

								// 'groundtruth_classes': a [num_boxes] int32 tensor storing the class

								//    labels of detected boxes in the image.

								// 'groundtruth_boxes': a [num_boxes, 4] float tensor storing the coordinates of

								//    detected boxes in the image.

								// 'groundtruth_instance_masks': (Optional), a [num_boxes, image_height,

								//    image_width] float tensor storing binary mask of the objects in boxes.


								// Instance mask format. Note that PNG masks are much more space efficient.

								enum InstanceMaskType {

								  DEFAULT = 0;          // Default implementation, currently NUMERICAL_MASKS

								  NUMERICAL_MASKS = 1;  // [num_masks, H, W] float32 binary masks.

								  PNG_MASKS = 2;        // Encoded PNG masks.

								}


								// Next id: 25

								message InputReader {

								  // Name of input reader. Typically used to describe the dataset that is read

								  // by this input reader.

								  optional string name = 23 [default=""];


								  // Path to StringIntLabelMap pbtxt file specifying the mapping from string

								  // labels to integer ids.

								  optional string label_map_path = 1 [default=""];


								  // Whether data should be processed in the order they are read in, or

								  // shuffled randomly.

								  optional bool shuffle = 2 [default=true];


								  // Buffer size to be used when shuffling.

								  optional uint32 shuffle_buffer_size = 11 [default = 2048];


								  // Buffer size to be used when shuffling file names.

								  optional uint32 filenames_shuffle_buffer_size = 12 [default = 100];


								  // The number of times a data source is read. If set to zero, the data source

								  // will be reused indefinitely.

								  optional uint32 num_epochs = 5 [default=0];


								  // Integer representing how often an example should be sampled. To feed

								  // only 1/3 of your data into your model, set `sample_1_of_n_examples` to 3.

								  // This is particularly useful for evaluation, where you might not prefer to

								  // evaluate all of your samples.

								  optional uint32 sample_1_of_n_examples = 22 [default=1];


								  // Number of file shards to read in parallel.

								  optional uint32 num_readers = 6 [default=64];


								  // Number of batches to produce in parallel. If this is run on a 2x2 TPU set

								  // this to 8.

								  optional uint32 num_parallel_batches = 19 [default=8];


								  // Number of batches to prefetch. Prefetch decouples input pipeline and

								  // model so they can be pipelined resulting in higher throughput. Set this

								  // to a small constant and increment linearly until the improvements become

								  // marginal or you exceed your cpu memory budget. Setting this to -1,

								  // automatically tunes this value for you.

								  optional int32 num_prefetch_batches = 20 [default=2];


								  // Maximum number of records to keep in reader queue.

								  optional uint32 queue_capacity = 3 [default=2000, deprecated=true];


								  // Minimum number of records to keep in reader queue. A large value is needed

								  // to generate a good random shuffle.

								  optional uint32 min_after_dequeue = 4 [default=1000, deprecated=true];


								  // Number of records to read from each reader at once.

								  optional uint32 read_block_length = 15 [default=32];


								  // Number of decoded records to prefetch before batching.

								  optional uint32 prefetch_size = 13 [default = 512, deprecated=true];


								  // Number of parallel decode ops to apply.

								  optional uint32 num_parallel_map_calls = 14 [default = 64, deprecated=true];


								  // If positive, TfExampleDecoder will try to decode rasters of additional

								  // channels from tf.Examples.

								  optional int32 num_additional_channels = 18 [default = 0];


								  // Number of groundtruth keypoints per object.

								  optional uint32 num_keypoints = 16 [default = 0];


								  // Maximum number of boxes to pad to during training / evaluation.

								  // Set this to at least the maximum amount of boxes in the input data,

								  // otherwise some groundtruth boxes may be clipped.

								  optional int32 max_number_of_boxes = 21 [default=100];


								  // Whether to load multiclass scores from the dataset.

								  optional bool load_multiclass_scores = 24 [default = false];


								  // Whether to load groundtruth instance masks.

								  optional bool load_instance_masks = 7 [default = false];


								  // Type of instance mask.

								  optional InstanceMaskType mask_type = 10 [default = NUMERICAL_MASKS];


								  // Whether to use the display name when decoding examples. This is only used

								  // when mapping class text strings to integers.

								  optional bool use_display_name = 17 [default = false];


								  oneof input_reader {

								    TFRecordInputReader tf_record_input_reader = 8;

								    ExternalInputReader external_input_reader = 9;

								  }

								}


								// An input reader that reads TF Example protos from local TFRecord files.

								message TFRecordInputReader {

								  // Path(s) to `TFRecordFile`s.

								  repeated string input_path = 1;

								}


								// An externally defined input reader. Users may define an extension to this

								// proto to interface their own input readers.

								message ExternalInputReader {

								  extensions 1 to 999;

								}