Training Toolbox for PyTorch provides a convenient environment to train Deep Learning models and convert them using OpenVINO™ Toolkit for optimized inference.
- Ubuntu 16.04 / 18.04
- Python 3.4-3.6
- libturbojpeg
- For Python pre-requisites refer to
requirements.txt - (Optional) OpenVINO™ R3 for exporting of the trained models
cd /<path_to_working_dir>/training_toolbox/pytorch_toolkit/<model>
bash init_venv.sh
- Start to work
. venv/bin/activate
Note: if you have installed the OpenVino toolkit after creating a virtual environment then you have to recreate one to install required packages for the Model Optimizer into one.
Do not forget to update several environment variables are required to compile and run OpenVINO™ toolkit applications, for details see: https://software.intel.com/en-us/articles/OpenVINO-Install-Linux.
After installation, you are ready to train your own models, evaluate and use them for prediction.
- Action recognition
- Face recognition
- Human pose estimation
- Instance segmentation
- Object Detection
- Segmentation of thoracic organs
- Super resolution
Tools are intended to perform manipulations with traned models, e.g. compress models using Quantization-aware training or sparsity.