https://github.com/open-edge-platform/training_extensions
Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
https://github.com/open-edge-platform/training_extensions
action-recognition anomaly-detection automl computer-vision datumaro deep-learning hyper-parameter-optimization image-classification image-segmentation incremental-learning machine-learning neural-networks-compression object-detection openvino pytorch quantization self-supervised-learning semi-supervised-learning transfer-learning
Last synced: 25 days ago
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Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
- Host: GitHub
- URL: https://github.com/open-edge-platform/training_extensions
- Owner: open-edge-platform
- License: apache-2.0
- Created: 2018-10-26T14:02:29.000Z (over 6 years ago)
- Default Branch: develop
- Last Pushed: 2025-04-03T20:09:36.000Z (about 1 month ago)
- Last Synced: 2025-04-03T21:23:21.471Z (about 1 month ago)
- Topics: action-recognition, anomaly-detection, automl, computer-vision, datumaro, deep-learning, hyper-parameter-optimization, image-classification, image-segmentation, incremental-learning, machine-learning, neural-networks-compression, object-detection, openvino, pytorch, quantization, self-supervised-learning, semi-supervised-learning, transfer-learning
- Language: Python
- Homepage: https://openvinotoolkit.github.io/training_extensions/
- Size: 409 MB
- Stars: 1,162
- Watchers: 48
- Forks: 449
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
- Security: security.md
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README
# OpenVINO™ Training Extensions
---
[Key Features](#key-features) •
[Installation](https://open-edge-platform.github.io/training_extensions/latest/guide/get_started/installation.html) •
[Documentation](https://open-edge-platform.github.io/training_extensions/latest/index.html) •
[License](#license)
[](https://pypi.org/project/otx)
[]()
[]()
[]()
[](https://codecov.io/gh/open-edge-platform/training_extensions)
[](https://securityscorecards.dev/viewer/?uri=github.com/open-edge-platform/training_extensions)
[](https://github.com/open-edge-platform/training_extensions/actions/workflows/pre_merge.yaml)
[](https://github.com/open-edge-platform/training_extensions/actions/workflows/docs.yaml)
[](https://opensource.org/licenses/Apache-2.0)
[](https://pepy.tech/project/otx)
---
## Introduction
OpenVINO™ Training Extensions is a low-code transfer learning framework for Computer Vision.
The API & 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](https://pytorch.org) and [OpenVINO™ toolkit](https://software.intel.com/en-us/openvino-toolkit).
OpenVINO™ Training Extensions provides a "recipe" 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](https://pytorch.org/vision/stable/index.html), [mmcv](https://github.com/open-mmlab/mmcv) and [OpenVINO Model Zoo (OMZ)](https://github.com/openvinotoolkit/open_model_zoo).
Furthermore, OpenVINO™ Training Extensions provides automatic configuration for ease of use.
The framework will analyze your dataset and identify the most suitable model and figure out the best input size setting and other hyper-parameters.
The development team is continuously extending this [Auto-configuration](https://open-edge-platform.github.io/training_extensions/latest/guide/explanation/additional_features/auto_configuration.html) 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
- **Anomaly recognition** tasks including anomaly classification, detection and segmentation
OpenVINO™ Training Extensions supports the [following learning methods](https://open-edge-platform.github.io/training_extensions/latest/guide/explanation/algorithms/index.html):
- **Supervised**, incremental training, which includes class incremental scenario.
OpenVINO™ Training Extensions provides the following usability features:
- [Auto-configuration](https://open-edge-platform.github.io/training_extensions/latest/guide/explanation/additional_features/auto_configuration.html). OpenVINO™ Training Extensions analyzes provided dataset and selects the proper task and model to provide the best accuracy/speed trade-off.
- [Datumaro](https://open-edge-platform.github.io/datumaro/stable/index.html) data frontend: OpenVINO™ Training Extensions supports the most common academic field dataset formats for each task. We are constantly working to extend supported formats to give more freedom of datasets format choice.
- **Distributed training** to accelerate the training process when you have multiple GPUs
- **Mixed-precision training** to save GPUs memory and use larger batch sizes
---
## Installation
Please refer to the [installation guide](https://open-edge-platform.github.io/training_extensions/latest/guide/get_started/installation.html).
If you want to make changes to the library, then a local installation is recommended.
Install from PyPI
Installing the library with pip is the easiest way to get started with otx.
```bash
pip install otx[base]
```
Alternatively, for zsh users:
```bash
pip install 'otx[base]'
```
Install from source
To install from source, you need to clone the repository and install the library using pip via editable mode.
```bash
# Use of virtual environment is highy recommended
# Using conda
yes | conda create -n otx_env python=3.10
conda activate otx_env
# Or using your favorite virtual environment
# ...
# Clone the repository and install in editable mode
git clone https://github.com/open-edge-platform/training_extensions.git
cd training_extensions
pip install -e .[base] # for zsh: pip install -e '.[base]'
```
---
## Quick-Start
OpenVINO™ Training Extensions supports both API and CLI-based training. The API is more flexible and allows for more customization, while the CLI training utilizes command line interfaces, and might be easier for those who would like to use OpenVINO™ Training Extensions off-the-shelf.
For the CLI, the commands below provide subcommands, how to use each subcommand, and more:
```bash
# See available subcommands
otx --help
# Print help messages from the train subcommand
otx train --help
# Print help messages for more details
otx train --help -v # Print required parameters
otx train --help -vv # Print all configurable parameters
```
You can find details with examples in the [CLI Guide](https://open-edge-platform.github.io/training_extensions/latest/guide/get_started/cli_commands.html). and [API Quick-Guide](https://open-edge-platform.github.io/training_extensions/latest/guide/get_started/api_tutorial.html).
Below is how to train with auto-configuration, which is provided to users with datasets and tasks:
Training via API
```python
# Training with Auto-Configuration via Engine
from otx.engine import Engine
engine = Engine(data_root="data/wgisd", task="DETECTION")
engine.train()
```
For more examples, see documentation: [API Quick-Guide](https://open-edge-platform.github.io/training_extensions/latest/guide/get_started/api_tutorial.html)
Training via CLI
```bash
otx train --data_root data/wgisd --task DETECTION
```
For more examples, see documentation: [CLI Guide](https://open-edge-platform.github.io/training_extensions/latest/guide/get_started/cli_commands.html)
In addition to the examples above, please refer to the documentation for tutorials on using custom models, training parameter overrides, and [tutorial per task types](https://open-edge-platform.github.io/training_extensions/latest/guide/tutorials/base/how_to_train/index.html), etc.
---
### Release History
Please refer to the [CHANGELOG.md](CHANGELOG.md)
---
## License
OpenVINO™ Toolkit is licensed under [Apache License Version 2.0](LICENSE).
By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
---
## Issues / Discussions
Please use [Issues](https://github.com/open-edge-platform/training_extensions/issues/new/choose) tab for your bug reporting, feature requesting, or any questions.
---
## Disclaimer
Intel is committed to respecting human rights and avoiding complicity in human rights abuses.
See Intel's [Global Human Rights Principles](https://www.intel.com/content/www/us/en/policy/policy-human-rights.html).
Intel's products and software are intended only to be used in applications that do not cause or contribute to a violation of an internationally recognized human right.
---
## Contributing
For those who would like to contribute to the library, see [CONTRIBUTING.md](CONTRIBUTING.md) for details.
Thank you! we appreciate your support!
---