| --- |
| 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 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. |
|
|
