# Configuring the Object Detection Training Pipeline

## Overview

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](https://arxiv.org/abs/1611.10012) 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 {
  }
}
```

### Model Parameter Initialization

While optional, it is highly recommended that users utilize other object
detection checkpoints. Training an object detector from scratch can take days.
To speed up the training process, it is recommended that users re-use the
feature extractor parameters from a pre-existing image classification or
object detection checkpoint. `train_config` provides two fields to specify
pre-existing checkpoints: `fine_tune_checkpoint` and
`from_detection_checkpoint`. `fine_tune_checkpoint` should provide a path to
the pre-existing checkpoint
(ie:"/usr/home/username/checkpoint/model.ckpt-#####").
`from_detection_checkpoint` is a boolean value. If false, it assumes the
checkpoint was from an object classification checkpoint. Note that starting
from a detection checkpoint will usually result in a faster training job than
a classification checkpoint.

The list of provided checkpoints can be found [here](detection_model_zoo.md).

### 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"`).
