Classifying ImageNet: using the C++ API

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 examples. 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 independent 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.