title	CaffeNet C++ Classification example
description	A simple example performing image classification using the low-level C++ API.
category	example
include_in_docs	true
priority	10

Classifying ImageNet: using the C++ API

Caffe, at its core, is written in C++. It is possible to use the C++ API of Caffe to implement an image classification application similar to the Python code presented in one of the Notebook example. To look at a more general-purpose example of the Caffe C++ API, you should study the source code of the command line tool caffe in tools/caffe.cpp.

Presentation

A simple C++ code is proposed in examples/cpp_classification/classification.cpp. For the sake of simplicity, this example does not support oversampling of a single sample nor batching of multiple independant samples. This example is not trying to reach the maximum possible classification throughput on a system, but special care was given to avoid unnecessary pessimization while keeping the code readable.

Compiling

The C++ example is built automatically when compiling Caffe. To compile Caffe you should follow the documented instructions. The classification example will be built as examples/classification.bin in your build directory.

Usage

To use the pre-trained CaffeNet model with the classification example, you need to download it from the "Model Zoo" using the following script:

./scripts/download_model_binary.py models/bvlc_reference_caffenet

The ImageNet labels file (also called the synset file) is also required in order to map a prediction to the name of the class:

./data/ilsvrc12/get_ilsvrc_aux.sh.

Using the files that were downloaded, we can classify the provided cat image (examples/images/cat.jpg) using this command:

./build/examples/cpp_classification/classification.bin \
  models/bvlc_reference_caffenet/deploy.prototxt \
  models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \
  data/ilsvrc12/imagenet_mean.binaryproto \
  data/ilsvrc12/synset_words.txt \
  examples/images/cat.jpg

The output should look like this:

---------- Prediction for examples/images/cat.jpg ----------
0.3134 - "n02123045 tabby, tabby cat"
0.2380 - "n02123159 tiger cat"
0.1235 - "n02124075 Egyptian cat"
0.1003 - "n02119022 red fox, Vulpes vulpes"
0.0715 - "n02127052 lynx, catamount"

Improving Performance

To further improve performance, you will need to leverage the GPU more, here are some guidelines:

• Move the data on the GPU early and perform all preprocessing operations there.
• If you have many images to classify simultaneously, you should use batching (independent images are classified in a single forward pass).
• Use multiple classification threads to ensure the GPU is always fully utilized and not waiting for an I/O blocked CPU thread.