AutoML Video Edge Library

AutoML Video Edge Library is an open source engine used for inferencing models trained using AutoML Video. It supports running Tensorflow, TF-TRT, TFLite, and EdgeTPU-optimized TFLite models.

I'm Developing For:

For Linux Desktop


If you are looking to do inferencing with no additional hardware, using only CPU then you may use the vanilla Tensorflow (.pb) and TFLite (.tflite) models.

Prerequisites

sudo apt-get update
sudo apt-get install python3.7
sudo apt-get install python3-pip
pip3 install opencv-contrib-python --user
pip3 install numpy

Note: opencv-contrib-python is only necessary for the examples, but can be excluded if only the library is being used.

If you plan on running TFLite models on the desktop, install the TFLite interpreter: https://www.tensorflow.org/lite/guide/python

If you plan on running Tensorflow models on desktop:
pip3 install tensorflow==1.14

Get the Code

git clone https://github.com/google/automl-video-ondevice

After that is done downloading, move into the directory.
cd automl-video-ondevice

Running an Example

For TFLite:
python3 examples/video_file_demo.py --model=data/traffic_model.tflite

For Tensorflow:
python3 examples/video_file_demo.py --model=data/traffic_model.pb

For Coral Device


Prerequisites

Make sure you've setup your coral device: https://coral.ai/docs/setup

Install the TFLite runtime on your device: https://www.tensorflow.org/lite/guide/python

sudo apt-get update
sudo apt-get install git
sudo apt-get install python3-opencv
pip3 install numpy

Get the Code

git clone https://github.com/google/automl-video-ondevice

After that is done downloading, move into the directory.
cd automl-video-ondevice

Running an Example

python3 examples/video_file_demo.py --model=data/traffic_model_edgetpu.tflite

For NVIDIA Jetson


Prerequisites

sudo apt-get update
sudo apt-get install git
sudo apt-get install python3-pip
sudo apt-get install python3-opencv
pip3 install numpy

Get the Code

git clone https://github.com/google/automl-video-ondevice

After that is done downloading, move into the directory.
cd automl-video-ondevice

Running an Example

python3 examples/video_file_demo.py --model=data/traffic_model_trt.pb