yigitcolakoglu
/
MyCity


								syntax = "proto2";


								package object_detection.protos;


								import "object_detection/protos/optimizer.proto";

								import "object_detection/protos/preprocessor.proto";


								// Message for configuring DetectionModel training jobs (train.py).

								// Next id: 28

								message TrainConfig {

								  // Effective batch size to use for training.

								  // For TPU (or sync SGD jobs), the batch size per core (or GPU) is going to be

								  // `batch_size` / number of cores (or `batch_size` / number of GPUs).

								  optional uint32 batch_size = 1 [default=32];


								  // Data augmentation options.

								  repeated PreprocessingStep data_augmentation_options = 2;


								  // Whether to synchronize replicas during training.

								  optional bool sync_replicas = 3 [default=false];


								  // How frequently to keep checkpoints.

								  optional float keep_checkpoint_every_n_hours = 4 [default=10000.0];


								  // Optimizer used to train the DetectionModel.

								  optional Optimizer optimizer = 5;


								  // If greater than 0, clips gradients by this value.

								  optional float gradient_clipping_by_norm = 6 [default=0.0];


								  // Checkpoint to restore variables from. Typically used to load feature

								  // extractor variables trained outside of object detection.

								  optional string fine_tune_checkpoint = 7 [default=""];


								  // Type of checkpoint to restore variables from, e.g. 'classification' or

								  // 'detection'. Provides extensibility to from_detection_checkpoint.

								  // Typically used to load feature extractor variables from trained models.

								  optional string fine_tune_checkpoint_type = 22 [default=""];


								  // [Deprecated]: use fine_tune_checkpoint_type instead.

								  // Specifies if the finetune checkpoint is from an object detection model.

								  // If from an object detection model, the model being trained should have

								  // the same parameters with the exception of the num_classes parameter.

								  // If false, it assumes the checkpoint was a object classification model.

								  optional bool from_detection_checkpoint = 8 [default=false, deprecated=true];


								  // Whether to load all checkpoint vars that match model variable names and

								  // sizes. This option is only available if `from_detection_checkpoint` is

								  // True.

								  optional bool load_all_detection_checkpoint_vars = 19 [default = false];


								  // Number of steps to train the DetectionModel for. If 0, will train the model

								  // indefinitely.

								  optional uint32 num_steps = 9 [default=0];


								  // Number of training steps between replica startup.

								  // This flag must be set to 0 if sync_replicas is set to true.

								  optional float startup_delay_steps = 10 [default=15];


								  // If greater than 0, multiplies the gradient of bias variables by this

								  // amount.

								  optional float bias_grad_multiplier = 11 [default=0];


								  // Variables that should be updated during training. Note that variables which

								  // also match the patterns in freeze_variables will be excluded.

								  repeated string update_trainable_variables = 25;


								  // Variables that should not be updated during training. If

								  // update_trainable_variables is not empty, only eliminates the included

								  // variables according to freeze_variables patterns.

								  repeated string freeze_variables = 12;


								  // Number of replicas to aggregate before making parameter updates.

								  optional int32 replicas_to_aggregate = 13 [default=1];


								  // Maximum number of elements to store within a queue.

								  optional int32 batch_queue_capacity = 14 [default=150, deprecated=true];


								  // Number of threads to use for batching.

								  optional int32 num_batch_queue_threads = 15 [default=8, deprecated=true];


								  // Maximum capacity of the queue used to prefetch assembled batches.

								  optional int32 prefetch_queue_capacity = 16 [default=5, deprecated=true];


								  // If true, boxes with the same coordinates will be merged together.

								  // This is useful when each box can have multiple labels.

								  // Note that only Sigmoid classification losses should be used.

								  optional bool merge_multiple_label_boxes = 17 [default=false];


								  // If true, will use multiclass scores from object annotations as ground

								  // truth. Currently only compatible with annotated image inputs.

								  optional bool use_multiclass_scores = 24 [default = false];


								  // Whether to add regularization loss to `total_loss`. This is true by

								  // default and adds all regularization losses defined in the model to

								  // `total_loss`.

								  // Setting this option to false is very useful while debugging the model and

								  // losses.

								  optional bool add_regularization_loss = 18 [default=true];


								  // Maximum number of boxes used during training.

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

								  // Otherwise, it may cause "Data loss: Attempted to pad to a smaller size

								  // than the input element" errors.

								  optional int32 max_number_of_boxes = 20 [default=100, deprecated=true];


								  // Whether to remove padding along `num_boxes` dimension of the groundtruth

								  // tensors.

								  optional bool unpad_groundtruth_tensors = 21 [default=true];


								  // Whether to retain original images (i.e. not pre-processed) in the tensor

								  // dictionary, so that they can be displayed in Tensorboard. Note that this

								  // will lead to a larger memory footprint.

								  optional bool retain_original_images = 23 [default=false];


								  // Whether to use bfloat16 for training. This is currently only supported for

								  // TPUs.

								  optional bool use_bfloat16 = 26 [default=false];


								  // Whether to summarize gradients.

								  optional bool summarize_gradients = 27 [default=false];

								}