chore: readme update#6878
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Pull request overview
Updates the repository root README to reflect the Geti™ project structure (application + getitune library), modernize quick-start guidance, and refresh badges/links to the correct documentation and ecosystem resources.
Changes:
- Reworked the README intro and navigation links to point to Geti™ and
getitunedocumentation. - Added “Quick start” sections for Geti™ (Windows/container/from source) and
getitune(PyPI install + minimal example). - Restructured Key Features into collapsible sections and refreshed community/ecosystem links.
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
kprokofi
reviewed
Jun 22, 2026
| [Documentation](#documentation) • | ||
| [Community](#community) | ||
| [Geti™ documentation](https://docs.geti.intel.com/) • | ||
| [`getitune` documentation](https://open-edge-platform.github.io/geti/latest/index.html) |
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This documentation is outdated, we don't have getitune documentation right now. Could you remove it?
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Instead there could be a link to README
| # Initialize and train using a bundled recipe and dataset | ||
| engine = create_engine( | ||
| data="tests/assets/classification_cifar10", | ||
| model="src/getitune/recipe/classification/multi_class_cls/efficientnet_b0.yaml", |
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Since a user may install it via pypi, the recipe path will be hard to access. I would rather to provide here an example with model name.
from getitune.utils import list_models
from getitune.engine import create_engine
from getitune.types import ExportFormat, ExportPrecision
# List all available models names
all_models = list_models()
# create Engine
engine = create_engine(
model="efficientnet_b0",
data="/path/to/dataset",
work_dir="./my_workspace",
)
# train a model
engine.train()
# Export to FP32 OpenVINO IR (default)
ov_ir_path = engine.export()
# validate engine
ov_engine = create_engine(
model="/path/to/exported_model.xml",
data="/path/to/dataset",
)
ov_engine.test() # test on test subset
ov_engine.predict() # predict on test subset
# optimize a model to int8 quantized version via NNCF tool
ov_engine.optimize()Signed-off-by: Barabanov, Alexander <alexander.barabanov@intel.com>
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Readme update.