Adopted from caffe's example
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
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.
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.
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.
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"
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.