YOLO ( is a neural network model that is able to recognise everyday objects very quickly from images. There’s also TinyYOLO (, which runs well on mobile devices. This guide tells you how to get TinyYOLO installed and running on your ODROID-XU4. To follow along, login to your ODROID, and run the commands show in the sections below.

Install TensorFlow

First, we make sure everything is up to date:

$ sudo apt-get update
$ sudo apt-get upgrade -y
$ sudo apt-get dist-upgrade -y
$ sudo reboot

Get some swap

Bazel won’t build without using swap memory on the ODROID-XU4. Pop in a blank 8GB USB drive, which will get erased, and run the following command:

$ sudo blkid
Check the device name, usually /dev/sda1, and with that name, run:
$ sudo mkswap /dev/sda1
$ sudo swapon /dev/sda1
$ sudo swapon

Install the requirements

We’ll need real Oracle Java, instead of OpenJDK. I tried OpenJDK, built Bazel with it, but it failed to SHA-1 hash downloads, and so was useless. So, we need to install the following packages:

$ sudo apt-get install pkg-config zip g++ zlib1g-dev unzip
$ sudo apt-get install gcc-4.8 g++-4.8
$ sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.8 100
$ sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.8 100
$ sudo apt-get install python-pip python-numpy swig python-dev
$ sudo pip install wheel
$ sudo add-apt-repository ppa:webupd8team/java
$ sudo apt-get update
$ sudo apt-get install oracle-java8-installer
$ sudo apt-get install oracle-java8-set-default
$ java -version

Install Bazel build system

Google builds things using Bazel. TensorFlow is from Google. Thus, we need to build Bazel first. This takes about a half an hours, go get some lunch while it runs:

$ wget
$ unzip -d bazel
$ cd bazel
$ sudo ./
Now, Java will run out of heap here, so we need to do the following modifications:
$ sudo vi scripts/bootstrap/
Find the line with “run” on it, and add some memory flags, change it to the following:
run “${JAVAC}” -J-Xms256m -J-Xmx384m -classpath “${classpath}” -sourcepath “${sourcepath}”
Then, compile again:
$ sudo ./
$ sudo cp output/bazel /usr/local/bin/bazel

Download and configure TensorFlow

Now we can actually download and configure TensorFlow:

$ git clone --recurse-submodules
$ cd tensorflow
I couldn’t get the latest version of TensorFlow to install, since it had BoringSSL C99 compile issues. To fix this, checkout version 1.4.0, and configure:
$ git checkout tags/v1.4.0
$ ./configure
Say no to most things, including OpenCL, as shown in Figure 1.
ODROID Magazine Figure 1 - Configuring TensorFlow
Figure 1 – Configuring TensorFlow

Build TensorFlow

Next, we need to build Tensorflow. If you thought Bazel took a long time to build, then you haven’t built software before. Hold onto your hats, because we’re in for a ride here:

$ bazel build -c opt --copt="-mfpu=neon-vfpv4" --copt="-funsafe-math-optimizations" --copt="-ftree-vectorize" --copt="-fomit-frame-pointer" --local_resources 8192,8.0,1.0 --verbose_failures tensorflow/tools/pip_package:build_pip_package
 1,900 / 4,909 files… error.
Oops, NEON doesn’t work. Ok, let’s turn that off. But, we’ll want to fix it later:
$ bazel build -c opt --copt="-funsafe-math-optimizations" --copt="-ftree-vectorize" --copt="-fomit-frame-pointer" --local_resources 8192,8.0,1.0 --verbose_failures tensorflow/tools/pip_package:build_pip_package
 3,700 / 4,622 files… error.
 In file included from tensorflow/compiler/xla/service/llvm_ir/
 ./tensorflow/core/lib/core/casts.h: In instantiation of 'Dest tensorflow::bit_cast(const Source&) [with Dest = long long int; Source = void (*)(const char*, long long int)]':
 tensorflow/compiler/xla/service/llvm_ir/ required from here
 ./tensorflow/core/lib/core/casts.h:91:3: error: static assertion failed: Sizes do not match
In this case, XLA is causing problems. It’s new, and not needed, so let’s drop it for now and reconfigure and rebuild without it:

2,345 / 3,683 files… 3,112 / 3,683 files… 3,682 / 3,683 files…

Target //tensorflow/tools/pip_package:build_pip_package up-to-date: bazel-bin/tensorflow/tools/pip_package/build_pip_package

Next, install it:

$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
$ sudo pip2 install /tmp/tensorflow_pkg/tensorflow-1.4.0-cp27-cp27mu-linux_armv7l.whl --upgrade --ignore-installed
At first when I ran the following command, it ran using python 3 and failed to install, so after some googling and learning about pip filename rules, I figured it out and just used pip2 instead:
$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.4.0-cp27-cp27mu-linux_armv7l.whl --upgrade --ignore-installed
Then, the most fun issue was when I first ran “import tensorflow”, when I got this message:
>>> import tensorflow
 Traceback (most recent call last):
 File “”, line 1, in 
 File “tensorflow/”, line 24, in 
 from tensorflow.python import *
 File “tensorflow/python/”, line 49, in 
 from tensorflow.python import pywrap_tensorflow
 File “tensorflow/python/”, line 25, in 
 from tensorflow.python.platform import self_check
 ImportError: No module named platform
I googled the issue, and it seemed to be about locales, as shown at So, I set a locale first, after also seeing that it needed to be capital US (, and rebuilt, and it still gave me the same issue:
$ export LC_ALL=en_US.UTF-8
$ export LANG=en_us.UTF-8
The next day, with fresh googling powers, revealed that actualy, it was just that I was just running it in the build directory! It has a directory called tensorflow in it, and python was looking up into that to find things. So just changing directories to another fixed the issue.
$ python2
 >>> import tensorflow
 >>> print(tensorflow.__version__)
It looks like everything is working, so onto YOLOing.

Running YOLO

I’m sure there are a few implementations of YOLO out there by now, so let’s pick one from

$ git clone
$ cd basic-yolo-keras
Get weights from!ApLdDEW3ut5fec2OzK4S4RpT-SU, or raccoon from!ApLdDEW3ut5feoZAEUwmSMYdPlY
$ wget /tiny_yolo_features.h5
$ wget /tiny_yolo_raccoon.h5
Next, edit the configuration file, and change the model to “Tiny Yolo”:
$ vi config.json
Download a picture of a racoon:
$ wget
Then, run the script:
$ python2 -c config.json -i Racoon_in_Vancouver.jpg -w tiny_yolo_raccoon.h5
It’s missing the imgaug package, so we’ll add it and its dependencies:
$ sudo pip2 install imgaug
$ sudo pip2 install keras
$ sudo pip2 install h5py
Both h5py and scipy take a little while to install. Can it find the raccoon in Figure 2?
ODROID Magazine Figure 2 - A test image of a raccoon
Figure 2 – A test image of a raccoon
$ python2 -c config.json -i Racoon_in_Vancouver.jpg -w tiny_yolo_raccoon.h5
Yes, now that’s a detected raccoon!
ODROID Magazine Figure 3 - YOLO has detected the image of the raccoon
Figure 3 – YOLO has detected the image of the raccoon
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