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Co-authored-by: TF Object Detection Team <>
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Configuring the Object Detection Training Pipeline


The TensorFlow Object Detection API uses protobuf files to configure the training and evaluation process. The schema for the training pipeline can be found in object_detection/protos/pipeline.proto. At a high level, the config file is split into 5 parts:

  1. The model configuration. This defines what type of model will be trained (ie. meta-architecture, feature extractor).
  2. The train_config, which decides what parameters should be used to train model parameters (ie. SGD parameters, input preprocessing and feature extractor initialization values).
  3. The eval_config, which determines what set of metrics will be reported for evaluation.
  4. The train_input_config, which defines what dataset the model should be trained on.
  5. The eval_input_config, which defines what dataset the model will be evaluated on. Typically this should be different than the training input dataset.

A skeleton configuration file is shown below:

model {
(... Add model config here...)

train_config : {
(... Add train_config here...)

train_input_reader: {
(... Add train_input configuration here...)

eval_config: {

eval_input_reader: {
(... Add eval_input configuration here...)

Picking Model Parameters

There are a large number of model parameters to configure. The best settings will depend on your given application. Faster R-CNN models are better suited to cases where high accuracy is desired and latency is of lower priority. Conversely, if processing time is the most important factor, SSD models are recommended. Read our paper for a more detailed discussion on the speed vs accuracy tradeoff.

To help new users get started, sample model configurations have been provided in the object_detection/samples/configs folder. The contents of these configuration files can be pasted into model field of the skeleton configuration. Users should note that the num_classes field should be changed to a value suited for the dataset the user is training on.

Defining Inputs

The TensorFlow Object Detection API accepts inputs in the TFRecord file format. Users must specify the locations of both the training and evaluation files. Additionally, users should also specify a label map, which define the mapping between a class id and class name. The label map should be identical between training and evaluation datasets.

An example input configuration looks as follows:

tf_record_input_reader {
  input_path: "/usr/home/username/data/train.record"
label_map_path: "/usr/home/username/data/label_map.pbtxt"

Users should substitute the input_path and label_map_path arguments and insert the input configuration into the train_input_reader and eval_input_reader fields in the skeleton configuration. Note that the paths can also point to Google Cloud Storage buckets (ie. "gs://project_bucket/train.record") for use on Google Cloud.

Configuring the Trainer

The train_config defines parts of the training process:

  1. Model parameter initialization.
  2. Input preprocessing.
  3. SGD parameters.

A sample train_config is below:

batch_size: 1
optimizer {
  momentum_optimizer: {
    learning_rate: {
      manual_step_learning_rate {
        initial_learning_rate: 0.0002
        schedule {
          step: 0
          learning_rate: .0002
        schedule {
          step: 900000
          learning_rate: .00002
        schedule {
          step: 1200000
          learning_rate: .000002
    momentum_optimizer_value: 0.9
  use_moving_average: false
fine_tune_checkpoint: "/usr/home/username/tmp/model.ckpt-#####"
from_detection_checkpoint: true
load_all_detection_checkpoint_vars: true
gradient_clipping_by_norm: 10.0
data_augmentation_options {
  random_horizontal_flip {

Input Preprocessing

The data_augmentation_options in train_config can be used to specify how training data can be modified. This field is optional.

SGD Parameters

The remainings parameters in train_config are hyperparameters for gradient descent. Please note that the optimal learning rates provided in these configuration files may depend on the specifics of the training setup (e.g. number of workers, gpu type).

Configuring the Evaluator

The main components to set in eval_config are num_examples and metrics_set. The parameter num_examples indicates the number of batches ( currently of batch size 1) used for an evaluation cycle, and often is the total size of the evaluation dataset. The parameter metrics_set indicates which metrics to run during evaluation (i.e. "coco_detection_metrics").