GitHub - open-edge-platform/geti: Build, train, optimize, and run computer vision models locally, from raw images to live inference. Open source, optimized for Intel XPU (CPU-only and CUDA also supported).

Geti™ is an end-to-end platform for building AI computer vision models.

Available as a Docker container or native Windows application, Geti™ guides you through the entire model lifecycle—from dataset preparation and training to optimization and deployment. Geti™ is optimized for fine-tuning and fast inference across the full Intel® XPU portfolio.

The Geti™ application is powered by getitune, an open-source engine for model training and optimization, which is also available standalone as a Python library. Geti™ and getitune are both developed in this repository, in the application and library folders, respectively.

Quick start with Geti™

There are several ways to run Geti™, choose the one that best fits your workflow:

• Install as a Windows application (MSIX)
• Run as a Docker container
• Install and launch via a script
• Build and run from source (for development)

For complete, step-by-step instructions - including prerequisites, GPU/accelerator support, container and source builds, the install script, and troubleshooting - see the Installation guide. To update an existing Geti™ installation to a newer version, follow the Upgrade guide.

Once Geti™ is up and running, follow the intuitive UI to train your first model.

Quick start with Geti Library (getitune)

Geti's training engine is published on PyPI and can train, optimize, and deploy models from Python.

uv pip install "getitune[xpu]" --extra-index-url https://download.pytorch.org/whl/xpu    # for Intel® XPU acceleration
uv pip install "getitune[cuda]" --extra-index-url https://download.pytorch.org/whl/cu128    # for NVIDIA® CUDA acceleration
uv pip install getitune # CPU-only by default

Discover available models and train a model in just a few lines of code:

from getitune.engine import create_engine
from getitune.utils import list_models

# Explore available models for your task
all_models = list_models()                    # List all model names
detection_models = list_models(task="DETECTION")  # Filter by task
recipes = list_models(return_recipes=True)    # Get full recipe YAML paths

# Create an engine using any model name or recipe path
engine = create_engine(
    model="efficientnet_b0",                  # model name, recipe YAML path, or exported IR/ONNX
    data="/path/to/dataset",                  # dataset directory or YAML path
    work_dir="./my_workspace",                # checkpoints and logs directory
    device="auto",                            # "auto", "cpu", "gpu", "xpu".
)

# Train and validate
engine.train(max_epochs=50)
metrics = engine.test()

# Export to OpenVINO IR (default) for deployment
exported_model_path = engine.export()

# load exported OpenVINO model
ov_engine = create_engine(model=exported_model_path, data=engine.datamodule)

# optimize the model for edge deployment
optimized_model_path = ov_engine.optimize()

# test the optimized model
metrics = ov_engine.test()

# predict with the optimized model
predictions = ov_engine.predict()

See the getitune documentation for the full list of recipes, advanced configuration, dataset support, backend-specific options, and deployment/optimization examples.

Key Features

Ecosystem

Explore other open-source AI projects by Intel®:

• Anomalib - An anomaly detection suite comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization and edge inference.
• Instant Learn - A framework for developing, benchmarking, and deploying zero-shot visual prompting algorithms on the edge.
• Datumaro - Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.
• OpenVINO™ - Software toolkit for optimizing and deploying deep learning models.
• OpenVINO™ Model Server - A scalable inference server for models optimized with OpenVINO™.
• Model API - A set of wrapper classes for particular tasks and model architectures, simplifying data preprocessing and postprocessing as well as routine procedures.
• Physical AI Studio - An end-to-end framework for teaching robots to perform tasks through imitation learning from human demonstrations.

Who uses Geti™?

Geti™ is used by research institutes, industrial partners, universities and AI enthusiasts. Applications range from robotics to medical analysis or industrial quality control. There are some interesting members of the Geti™ community:

• Intel Foundry
• Royal Brompton and Harefield hospitals
• WSC Sports
• Dell NativeEdge
• Bravent
• ASRock Industrial
• PeopleSense.AI
• Capgemini

Contribute

To report a bug or submit a feature request, please open a GitHub issue. If you have an open question, ask in GitHub Discussions.

For developers who would like to contribute with a pull request, see the Contributing guide for details.

Thank you 👏 to all our contributors!

License

Geti™ is licensed under the Apache License Version 2.0.

Disclaimers

Geti™ utilizes FFmpeg.

FFmpeg is an open source project licensed under LGPL and GPL. See https://www.ffmpeg.org/legal.html. You are solely responsible for determining if your use of FFmpeg requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of FFmpeg.