OpenVINO™ Training Extensions

Key Features • Installation • Documentation • License

Introduction

OpenVINO™ Training Extensions is a low-code transfer learning framework for Computer Vision. The CLI commands of the framework allows users to train, infer, optimize and deploy models easily and quickly even with low expertise in the deep learning field. OpenVINO™ Training Extensions offers diverse combinations of model architectures, learning methods, and task types based on PyTorch and OpenVINO™ toolkit.

OpenVINO™ Training Extensions provides a "model template" for every supported task type, which consolidates necessary information to build a model. Model templates are validated on various datasets and serve one-stop shop for obtaining the best models in general. If you are an experienced user, you can configure your own model based on torchvision, pytorchcv, mmcv and OpenVINO Model Zoo (OMZ).

Furthermore, OpenVINO™ Training Extensions provides automatic configuration of task types and hyperparameters. The framework will identify the most suitable model template based on your dataset, and choose the best hyperparameter configuration. The development team is continuously extending functionalities to make training as simple as possible so that single CLI command can obtain accurate, efficient and robust models ready to be integrated into your project.

Key Features

OpenVINO™ Training Extensions supports the following computer vision tasks:

Classification, including multi-class, multi-label and hierarchical image classification tasks.
Object detection including rotated bounding box support
Semantic segmentation
Instance segmentation including tiling algorithm support
Action recognition including action classification and detection
Anomaly recognition tasks including anomaly classification, detection and segmentation

OpenVINO™ Training Extensions supports the following learning methods:

Supervised, incremental training, which includes class incremental scenario and contrastive learning for classification and semantic segmentation tasks
Semi-supervised learning
Self-supervised learning

OpenVINO™ Training Extensions will provide the following features in coming releases:

Distributed training to accelerate the training process when you have multiple GPUs
Half-precision training to save GPUs memory and use larger batch sizes
Integrated, efficient hyper-parameter optimization module (HPO). Through dataset proxy and built-in hyper-parameter optimizer, you can get much faster hyper-parameter optimization compared to other off-the-shelf tools. The hyperparameter optimization is dynamically scheduled based on your resource budget.
OpenVINO™ Training Extensions uses Datumaro as the backend to hadle datasets. Thanks to that, OpenVINO™ Training Extensions supports the most common academic field dataset formats for each task. We constantly working to extend supported formats to give more freedom of datasets format choice.
Auto-configuration functionality. OpenVINO™ Training Extensions analyzes provided dataset and selects the proper task and model template to provide the best accuracy/speed trade-off. It will also make a random auto-split of your dataset if there is no validation set provided.

Getting Started

Installation

Please refer to the installation guide for installing it using Python venv (virtual environment), or use the following steps to run it in a containerised environment:

$ docker build \
    -t trainer \ # image tag, required
    --build-arg UBUNTU_VER=20.04 \ # default Ubunutu version, optional
    --build-arg PYTHON_VER=3.9 \ # default Python version, optional
    --build-arg SOURCE=https://download.pytorch.org/whl/cpu \ # default (CPU) deps, optional
    . # training_extensions/
$ docker run \
    -it \ # enter interactive terminal
    --rm \ # remove container after use
    -v "$(pwd)/shared:/mnt/shared:rw" \ # shared volume to host machine
    --shm-size=4g \ # increase mounted shared memory
    trainer
trainer$ otx # ... installed on Ubuntu 20.04 with /mnt/shared as shared directory

Note: Python 3.8 and 3.9 were tested, along with Ubuntu 18.04 and 20.04.