Geti™ is an open-source, end-to-end platform for building and continuously improving your own computer vision models. It covers annotation, fine-tuning, optimization, deployment, and inference — with 1,600+ model variants for detection, classification, and segmentation, optimized by OpenVINO™ for Intel® CPUs, GPUs, and NPUs.
The Geti™ application is powered by getitune, its engine for model training and optimization, which is also available standalone as a Python library. Choose the application for a no-code experience, or the getitune Python API for code-first workflows — both paths lead to production-ready models. Both are developed in this repository, under application/ and library/.
The Geti™ application runs as a native Windows app or a pre-built Docker image for Linux.
- 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.
Tip
The documentation is a valuable resource to learn more about Geti™ and its capabilities.
New users are encouraged to read the step-by-step tutorial "Train your first model".
Geti's training engine is also available on PyPI as getitune, an open-source Python library for training, optimizing, and deploying models.
Install getitune and run the demo code
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[!IMPORTANT]
The PyPI package does NOT include Ultralytics YOLO models, which are distributed under the AGPL-3.0 license. To enable these models, build from source with theultralyticsextra as explained in the getitune documentation.
Pick a model, then train, export, and quantize it:
from getitune.engine import create_engine
from getitune.utils import list_models
# Explore available models to pick the one you need
all_models = list_models() # All model names
detection_models = list_models(task="DETECTION") # Filter by task
recipes = list_models(return_recipes=True) # Full recipe YAML paths
# Choose a model and point it at your dataset
engine = create_engine(
model="edgecrafter_m",
data="./led_counting",
work_dir="./my_workspace", # checkpoints, exports, and logs
device="auto", # "auto", "cpu", "gpu", or "xpu"
)
# Train the model on your own Intel CPU or GPU
engine.train()
# Export the trained model to OpenVINO format (.xml + .bin)
exported_model_path = engine.export()
# Load the exported model into the OpenVINO engine
openvino_engine = create_engine(model=exported_model_path, data=engine.datamodule)
# Quantize the model to INT8, optimized for Intel hardware
optimized_model_path = openvino_engine.optimize()
# Run inference with the optimized INT8 model
predictions = openvino_engine.predict(checkpoint=optimized_model_path)See the getitune documentation for the full list of recipes, advanced configuration, dataset support, backend-specific options, and deployment/optimization examples.
🏆 State-of-the-art model catalog
Train and fine-tune modern architectures such as RF-DETR, DINOv3 DETR, YOLO26, YOLOX, D-FINE, and Mask R-CNN. Would you like to see a specific model added? Let us know by opening a GitHub issue!
| Computer Vision Task | Model Architecture | Paper |
|---|---|---|
| Object Detection Locate and classify objects with bounding boxes. Common use cases: counting items, defect localization, surveillance. |
D-FINE M / L / X | DEIM + D-FINE |
| DINOv3 DETR S / M / L | DINOv3 + DEIMv2 + DETR | |
| ECDet S / M / L / X | EdgeCrafter | |
| MobileNet V2 ATSS | MobileNetV2 + ATSS | |
| MobileNet V2 SSD | MobileNetV2 + SSD | |
| RF-DETR N / S / M / L | RF-DETR | |
| RT-DETR R50 | RT-DETR | |
| YOLO11 N / S / M / L / X | Ultralytics YOLO11 | |
| YOLO12 N / S / M / L / X | YOLOv12 | |
| YOLO26 N / S / M / L / X | YOLO26 | |
| YOLOX Tiny / S / L / X | YOLOX | |
| Instance Segmentation Detect objects and produce pixel-precise masks per instance. Common use cases: medical imaging, robotics, area estimation. |
RTMDet Tiny | RTMDet |
| Mask-RCNN EfficientNet B2 | EfficientNet + Mask R-CNN | |
| Mask-RCNN ResNet50 | ResNet + Mask R-CNN | |
| Mask-RCNN Swin-T | Swin Transformer + Mask R-CNN | |
| RF-DETR N / S / M / L / XL / 2XL | RF-DETR | |
| YOLO11 N / S / M / L / X | Ultralytics YOLO11 | |
| YOLO26 N / S / M / L / X | YOLO26 | |
| Classification (multi-class, multi-label) Assign one or more labels to an entire image. Common use cases: defect classification, product categorization, content tagging. |
ViT Tiny | ViT |
| DINOv2 Small | DINOv2 | |
| EfficientNet B0 / B3 | EfficientNet | |
| EfficientNet V2 Small | EfficientNetV2 | |
| MobileNet V3 Large | MobileNetV3 | |
| YOLO26 N / S / M / L / X | YOLO26 | |
| Other models from timm (1600+ backbones) | timm |
🔄 Interactive end-to-end model training
Geti™ enables users to start building deep-learning computer vision models with as few as 10-20 images and take them to production in one environment - annotate, train, optimize, run inference, and improve accuracy in a rapid train-predict-annotate loop.
⚡ Hardware-accelerated inference & model optimization
Every model is automatically exported with OpenVINO™ for deployment across the full Intel® XPU portfolio (Arc™ GPUs, Core™ Ultra processors); NVIDIA® CUDA and CPU-only execution are also supported. Fine-tune and run inference directly on edge and client hardware - including Intel® Panther Lake and Arc™ Battlemage (B-series) GPUs - with no Kubernetes cluster or data-center GPU required. Built-in accuracy-aware INT8 quantization further reduces model size and latency on resource-constrained edge devices with minimal impact on accuracy.
🚀 Integrated deployment & inference
Build custom pipelines (source → model → sink) to deploy models inside Geti and monitor real-time predictions on video streams. Sources include USB/IP cameras and video files; optional sinks include folder, MQTT, and webhook. Complete pipelines can be exported as OpenVINO™-optimized bundles for edge deployment.
🎨 Multiple computer vision tasks
Geti™ supports multiple computer vision tasks that are commonly employed across various use cases - image classification, object detection and instance segmentation from the no-code web interface, with even more tasks available through the getitune library.
🧠 Smart annotations
Smart annotations in Geti™ enable users to easily create bounding boxes and polygons. These smart annotation features coupled with the AI-assisted annotations and state-of-the-art AI models such as the Segment Anything Model keep human experts in the loop while massively reducing the total annotation efforts needed by a human.
📦 Model & dataset management
Track how datasets and models evolve, link models to a specific dataset revision, view exact training hyperparameters, and fine-tune from any previous version. Import and export in COCO, Pascal VOC, YOLO, and a Geti-optimized native format, with label filtering to selectively include or exclude labels on import/export.
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.
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
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!
Geti™ is licensed under the Apache License Version 2.0.
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.
Note
Ultralytics YOLO models are distributed under the AGPL-3.0 license, an OSI approved license ideal for open-source research, academic, and personal projects. For commercial use, enhanced support, and tailored licensing terms, please explore flexible Ultralytics licensing options at https://www.ultralytics.com/license.