computervision

We're doing some computer vision stuff at iNat.

models

We don't share our models publicly. You could train your own models or perhaps find one from one of the model zoos and adapt it.

os x dependencies

• brew install libmagic

python

• python3 -m venv venv
• source ./venv/bin/activate
• pip3 install -U pip
• pip3 install -r requirements.txt

installation

Here's a rough script for OS X assuming you already have homebrew, Python, and virtualenv installed.

# Get dependencies
brew install libmagic

# Get the repo
git clone git@github.com:inaturalist/inatVisionAPI.git
cd inatVisionAPI/

# Set up your python environment
python3 -m venv venv
source venv/bin/activate
pip3 install -U pip
pip3 install -r requirements.txt

# Copy your config file (and edit, of course)
cp config.yml.example config.yml

# Run the app
python app.py

Now you should be able to test at http://localhost:6006 via the browser.

Notes

If the device you're installing on has AVX extensions (check flags in /proc/cpuinfo), try compiling tensorflow for better performance: https://www.tensorflow.org/install/install_sources This is a good idea on AWS or bare metal, but won't make a difference on Rackspace due to them using an old hypervisor. If you're not compiling, install tensorflow from pip: pip install tensorflow

If the device you're installing on has AVX2 or SSE4, install pillow-simd for faster image resizing: pip install pillow-simd if you only have SSE4, or CC="cc -mavx2" pip install pillow-simd if you have AVX2. I saw a significant increase in performance from pillow to pillow-simd with SSE4, less of an increase for AVX2. otherwise, install pillow from pip: pip install pillow

tensorflow seems to want to compile against your system copy of numpy on OS X regardless of the virtualenv, so if you see stupid errors like ImportError: numpy.core.multiarray failed to import, try running deactivate to get out the virtualenv, then pip install -U numpy or somesuch to update your system copy of numpy. Then source inatvision-venv/bin/activate to get back in your virtualend and try again.

Some performance data from a 15" MBP, 2.5GHz i7:

task	pip tensorflow	compiled tensorflow	compiled tensorflow + pillow-simd
100x medium.jpg	25 seconds	17 seconds	15 seconds
100x iphone photos	81 seconds	72 seconds	46 seconds

The larger the images coming into the pipeline, the more important optimized resize (like pillow-simd) is.