STT-tensorflow/tensorflow/lite/examples/label_image

TensorFlow Lite C++ image classification demo

This example shows how you can load a pre-trained and converted TensorFlow Lite model and use it to recognize objects in images.

Before you begin, make sure you have TensorFlow installed.

You also need to install Bazel in order to build this example code. And be sure you have the Python future module installed:

pip install future --user

Build the example

First run $TENSORFLOW_ROOT/configure. To build for Android, set Android NDK or configure NDK setting in $TENSORFLOW_ROOT/WORKSPACE first.

Build it for desktop machines (tested on Ubuntu and OS X):

bazel build -c opt //tensorflow/lite/examples/label_image:label_image

Build it for Android ARMv8:

bazel build -c opt --config=android_arm64 \
  //tensorflow/lite/examples/label_image:label_image

Build it for Android arm-v7a:

bazel build -c opt --config=android_arm \
  //tensorflow/lite/examples/label_image:label_image

Download sample model and image

You can use any compatible model, but the following MobileNet v1 model offers a good demonstration of a model trained to recognize 1,000 different objects.

# Get model
curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz | tar xzv -C /tmp

# Get labels
curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz  | tar xzv -C /tmp  mobilenet_v1_1.0_224/labels.txt

mv /tmp/mobilenet_v1_1.0_224/labels.txt /tmp/

Run the sample on a desktop

bazel-bin/tensorflow/lite/examples/label_image/label_image \
  --tflite_model /tmp/mobilenet_v1_1.0_224.tflite \
  --labels /tmp/labels.txt \
  --image tensorflow/lite/examples/label_image/testdata/grace_hopper.bmp

You should see results like this:

Loaded model /tmp/mobilenet_v1_1.0_224.tflite
resolved reporter
invoked
average time: 68.12 ms
0.860174: 653 653:military uniform
0.0481017: 907 907:Windsor tie
0.00786704: 466 466:bulletproof vest
0.00644932: 514 514:cornet, horn, trumpet, trump
0.00608029: 543 543:drumstick

Run the sample on an Android device

Prepare data on devices, e.g.,

adb push bazel-bin/tensorflow/lite/examples/label_image/label_image  /data/local/tmp
adb push /tmp/mobilenet_v1_1.0_224.tflite  /data/local/tmp
adb push tensorflow/lite/examples/label_image/testdata/grace_hopper.bmp  /data/local/tmp
adb push /tmp/labels.txt /data/local/tmp

Run it,

adb shell "/data/local/tmp/label_image \
    -m /data/local/tmp/mobilenet_v1_1.0_224.tflite \
    -i /data/local/tmp/grace_hopper.bmp \
    -l /data/local/tmp/labels.txt"

then you should see something like the following:

Loaded model /data/local/tmp/mobilenet_v1_1.0_224.tflite
resolved reporter
INFO: Initialized TensorFlow Lite runtime.
invoked
average time: 25.03 ms
0.907071: 653 military uniform
0.0372416: 907 Windsor tie
0.00733753: 466 bulletproof vest
0.00592852: 458 bow tie
0.00414091: 514 cornet

Run the model with NNAPI delegate (-a 1),

adb shell "/data/local/tmp/label_image \
    -m /data/local/tmp/mobilenet_v1_1.0_224.tflite \
    -i /data/local/tmp/grace_hopper.bmp \
    -l /data/local/tmp/labels.txt -a 1 -f 1"

then you should see something like the following:

Loaded model /data/local/tmp/mobilenet_v1_1.0_224.tflite
resolved reporter
INFO: Initialized TensorFlow Lite runtime.
INFO: Created TensorFlow Lite delegate for NNAPI. Applied NNAPI delegate.
invoked
average time:10.348 ms
0.905401: 653 military uniform
0.0379589: 907 Windsor tie
0.00735866: 466 bulletproof vest
0.00605307: 458 bow tie
0.00422573: 514 cornet

To run a model with the Hexagon Delegate, assuming we have followed the Hexagon Delegate Guide and installed Hexagon libraries in /data/local/tmp. Run it with (-j 1)

adb shell \
    "/data/local/tmp/label_image \
    -m /data/local/tmp/mobilenet_v1_1.0_224_quant.tflite \
    -i /data/local/tmp/grace_hopper.bmp \
    -l /data/local/tmp/labels.txt -j 1"

then you should see something like the followings:

Loaded model /data/local/tmp/mobilenet_v1_1.0_224_quant.tflite
resolved reporter
INFO: Initialized TensorFlow Lite runtime.
loaded libcdsprpc.so
INFO: Created TensorFlow Lite delegate for Hexagon.
INFO: Hexagon delegate: 31 nodes delegated out of 31 nodes with 1 partitions.
Applied Hexagon delegate.
invoked
average time: 4.231 ms
0.639216: 458 bow tie
0.329412: 653 military uniform
0.00784314: 835 suit
0.00784314: 611 jersey
0.00392157: 514 cornet

Run the model with the XNNPACK delegate (-x 1),

adb shell \
    "/data/local/tmp/label_image \
    -m /data/local/tmp/mobilenet_v1_1.0_224.tflite \
    -i /data/local/tmp/grace_hopper.bmp \
    -l /data/local/tmp/labels.txt -x 1"

then you should see something like the following:

Loaded model /data/local/tmp/mobilenet_v1_1.0_224.tflite
resolved reporter
INFO: Initialized TensorFlow Lite runtime. Applied XNNPACK delegate.
invoked
average time: 17.33 ms
0.90707: 653 military uniform
0.0372418: 907 Windsor tie
0.0073376: 466 bulletproof vest
0.00592857: 458 bow tie
0.00414093: 514 cornet

With -h or any other unsupported flags, label_image will list supported options:

sargo:/data/local/tmp $ ./label_image -h
./label_image: invalid option -- h
label_image
--accelerated, -a: [0|1], use Android NNAPI or not
--old_accelerated, -d: [0|1], use old Android NNAPI delegate or not
--allow_fp16, -f: [0|1], allow running fp32 models with fp16 or not
--count, -c: loop interpreter->Invoke() for certain times
--gl_backend, -g: [0|1]: use GL GPU Delegate on Android
--hexagon_delegate, -j: [0|1]: use Hexagon Delegate on Android
--input_mean, -b: input mean
--input_std, -s: input standard deviation
--image, -i: image_name.bmp
--labels, -l: labels for the model
--tflite_model, -m: model_name.tflite
--profiling, -p: [0|1], profiling or not
--num_results, -r: number of results to show
--threads, -t: number of threads
--verbose, -v: [0|1] print more information
--warmup_runs, -w: number of warmup runs
--xnnpack_delegate, -x [0:1]: xnnpack delegate`

See the label_image.cc source code for other command line options.

Note that this binary also supports runtime/delegate arguments introduced by the delegate registrar. If there is any conflict, the arguments mentioned earlier are given precedence. For example, you can run the binary with additional command line options such as --use_nnapi=true --nnapi_accelerator_name=google-edgetpu to utilize the EdgeTPU in a 4th-gen Pixel phone. Please be aware that the "=" in the option should not be omitted.

adb shell \
    "/data/local/tmp/label_image \
    -m /data/local/tmp/mobilenet_v1_1.0_224_quant.tflite \
    -i /data/local/tmp/grace_hopper.bmp \
    -l /data/local/tmp/labels.txt -j 1 \
    --use_nnapi=true --nnapi_accelerator_name=google-edgetpu"