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