TensorFlow C++ Image Recognition Demo

This example shows how you can load a pre-trained TensorFlow network and use it to recognize objects in images in C++. For Java see the Java README, and for Go see the godoc example.


This demo uses a Google Inception model to classify image files that are passed in on the command line.

To build/install/run

The TensorFlow GraphDef that contains the model definition and weights is not packaged in the repo because of its size. Instead, you must first download the file to the data directory in the source tree:

$ wget -O tensorflow/examples/label_image/data/

$ unzip tensorflow/examples/label_image/data/ -d tensorflow/examples/label_image/data/

Then, as long as you've managed to build the main TensorFlow framework, you should have everything you need to run this example installed already.

Once extracted, see the labels file in the data directory for the possible classifications, which are the 1,000 categories used in the Imagenet competition.

To build it, run this command:

$ bazel build tensorflow/examples/label_image/...

That should build a binary executable that you can then run like this:

$ bazel-bin/tensorflow/examples/label_image/label_image

This uses the default example image that ships with the framework, and should output something similar to this:

I tensorflow/examples/label_image/] military uniform (866): 0.647299
I tensorflow/examples/label_image/] suit (794): 0.0477195
I tensorflow/examples/label_image/] academic gown (896): 0.0232407
I tensorflow/examples/label_image/] bow tie (817): 0.0157355
I tensorflow/examples/label_image/] bolo tie (940): 0.0145023

In this case, we're using the default image of Admiral Grace Hopper, and you can see the network correctly spots she's wearing a military uniform, with a high score of 0.6.

Next, try it out on your own images by supplying the --image= argument, e.g.

$ bazel-bin/tensorflow/examples/label_image/label_image --image=my_image.png

For a more detailed look at this code, you can check out the C++ section of the Inception tutorial.