Converting a Caffe* Model

A summary of the steps for optimizing and deploying a model that was trained with Caffe*:

  1. Configure the Model Optimizer for Caffe*.

  2. Convert a Caffe* Model to produce an optimized Intermediate Representation (IR) of the model based on the trained network topology, weights, and biases values

  3. Test the model in the Intermediate Representation format using the Inference Engine in the target environment via provided Inference Engine sample applications

  4. Integrate the Inference Engine in your application to deploy the model in the target environment

Supported Topologies

  • Classification models:

    • AlexNet

    • VGG-16, VGG-19

    • SqueezeNet v1.0, SqueezeNet v1.1

    • ResNet-50, ResNet-101, Res-Net-152

    • Inception v1, Inception v2, Inception v3, Inception v4

    • CaffeNet

    • MobileNet

    • Squeeze-and-Excitation Networks: SE-BN-Inception, SE-Resnet-101, SE-ResNet-152, SE-ResNet-50, SE-ResNeXt-101, SE-ResNeXt-50

    • ShuffleNet v2

  • Object detection models:

    • SSD300-VGG16, SSD500-VGG16

    • Faster-RCNN

    • RefineDet (MYRIAD plugin only)

  • Face detection models:

    • VGG Face

    • SSH: Single Stage Headless Face Detector

  • Semantic segmentation models:

    • FCN8


It is necessary to specify mean and scale values for most of the Caffe* models to convert them with the Model Optimizer. The exact values should be determined separately for each model. For example, for Caffe* models trained on ImageNet, the mean values usually are 123.68, 116.779, 103.939 for blue, green and red channels respectively. The scale value is usually 127.5. Refer to the General Conversion Parameters section in Converting a Model to Intermediate Representation (IR) for the information on how to specify mean and scale values.

Convert a Caffe* Model

To convert a Caffe* model, run Model Optimizer with the path to the input model .caffemodel file and the path to an output directory with write permissions:

cd <INSTALL_DIR>/deployment_tools/model_optimizer/
python3 --input_model <INPUT_MODEL>.caffemodel --output_dir <OUTPUT_MODEL_DIR>
mo --input_model <INPUT_MODEL>.caffemodel --output_dir <OUTPUT_MODEL_DIR>

Two groups of parameters are available to convert your model:

Using Caffe*-Specific Conversion Parameters

The following list provides the Caffe*-specific parameters.

Caffe*-specific parameters:
  --input_proto INPUT_PROTO, -d INPUT_PROTO
                        Deploy-ready prototxt file that contains a topology
                        structure and layer attributes
  --caffe_parser_path CAFFE_PARSER_PATH
                        Path to python Caffe parser generated from caffe.proto
  -k K                  Path to CustomLayersMapping.xml to register custom
  --mean_file MEAN_FILE, -mf MEAN_FILE
                        Mean image to be used for the input. Should be a
                        binaryproto file
  --mean_file_offsets MEAN_FILE_OFFSETS, -mo MEAN_FILE_OFFSETS
                        Mean image offsets to be used for the input
                        binaryproto file. When the mean image is bigger than
                        the expected input, it is cropped. By default, centers
                        of the input image and the mean image are the same and
                        the mean image is cropped by dimensions of the input
                        image. The format to pass this option is the
                        following: "-mo (x,y)". In this case, the mean file is
                        cropped by dimensions of the input image with offset
                        (x,y) from the upper left corner of the mean image
                        Disable omitting optional attributes to be used for
                        custom layers. Use this option if you want to transfer
                        all attributes of a custom layer to IR. Default
                        behavior is to transfer the attributes with default
                        values and the attributes defined by the user to IR.
                        Enable flattening optional params to be used for
                        custom layers. Use this option if you want to transfer
                        attributes of a custom layer to IR with flattened
                        nested parameters. Default behavior is to transfer the
                        attributes without flattening nested parameters.

Command-Line Interface (CLI) Examples Using Caffe*-Specific Parameters

  • Launching the Model Optimizer for the bvlc_alexnet.caffemodel with a specified prototxt file. This is needed when the name of the Caffe* model and the .prototxt file are different or are placed in different directories. Otherwise, it is enough to provide only the path to the input model.caffemodel file. You must have write permissions for the output directory.

    python3 --input_model bvlc_alexnet.caffemodel --input_proto bvlc_alexnet.prototxt --output_dir <OUTPUT_MODEL_DIR>
    mo --input_model bvlc_alexnet.caffemodel --input_proto bvlc_alexnet.prototxt --output_dir <OUTPUT_MODEL_DIR>
  • Launching the Model Optimizer for the bvlc_alexnet.caffemodel with a specified CustomLayersMapping file. This is the legacy method of quickly enabling model conversion if your model has custom layers. This requires the Caffe* system on the computer. To read more about this, see Legacy Mode for Caffe* Custom Layers. Optional parameters without default values and not specified by the user in the .prototxt file are removed from the Intermediate Representation, and nested parameters are flattened:

    python3 --input_model bvlc_alexnet.caffemodel -k CustomLayersMapping.xml --disable_omitting_optional --enable_flattening_nested_params --output_dir <OUTPUT_MODEL_DIR>
    mo --input_model bvlc_alexnet.caffemodel -k CustomLayersMapping.xml --disable_omitting_optional --enable_flattening_nested_params --output_dir <OUTPUT_MODEL_DIR>

    This example shows a multi-input model with input layers: data, rois

    layer {
      name: "data"
      type: "Input"
      top: "data"
      input_param {
        shape { dim: 1 dim: 3 dim: 224 dim: 224 }
    layer {
      name: "rois"
      type: "Input"
      top: "rois"
      input_param {
        shape { dim: 1 dim: 5 dim: 1 dim: 1 }
  • Launching the Model Optimizer for a multi-input model with two inputs and providing a new shape for each input in the order they are passed to the Model Optimizer along with a writable output directory. In particular, for data, set the shape to 1,3,227,227. For rois, set the shape to 1,6,1,1 :

    python3 --input_model /path-to/your-model.caffemodel --input data,rois --input_shape (1,3,227,227),[1,6,1,1] --output_dir <OUTPUT_MODEL_DIR>
    mo --input_model /path-to/your-model.caffemodel --input data,rois --input_shape (1,3,227,227),[1,6,1,1] --output_dir <OUTPUT_MODEL_DIR>

Custom Layer Definition

Internally, when you run the Model Optimizer, it loads the model, goes through the topology, and tries to find each layer type in a list of known layers. Custom layers are layers that are not included in the list of known layers. If your topology contains any layers that are not in this list of known layers, the Model Optimizer classifies them as custom.

Supported Caffe* Layers

Refer to Supported Framework Layers for the list of supported standard layers.

Frequently Asked Questions (FAQ)

The Model Optimizer provides explanatory messages if it is unable to run to completion due to issues like typographical errors, incorrectly used options, or other issues. The message describes the potential cause of the problem and gives a link to the Model Optimizer FAQ. The FAQ has instructions on how to resolve most issues. The FAQ also includes links to relevant sections in the Model Optimizer Developer Guide to help you understand what went wrong.


In this document, you learned:

  • Basic information about how the Model Optimizer works with Caffe* models

  • Which Caffe* models are supported

  • How to convert a trained Caffe* model using the Model Optimizer with both framework-agnostic and Caffe-specific command-line options