The Hailo Model Zoo includes pre-trained models and a full building and evaluation environment
Hailo Model Zoo ===============
.. |python| image:: https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-blue.svg :target: https://www.python.org/downloads/release/python-3123/ :alt: Python 3.12 :width: 150 :height: 20
.. |tensorflow| image:: https://img.shields.io/badge/Tensorflow-2.19.1-blue.svg :target: https://github.com/tensorflow/tensorflow/releases/tag/v2.19.1 :alt: Tensorflow :width: 110 :height: 20
.. |cuda| image:: https://img.shields.io/badge/CUDA-12.5.1-blue.svg :target: https://developer.nvidia.com/cuda-toolkit :alt: Cuda :width: 80 :height: 20
.. |compiler| image:: https://img.shields.io/badge/Hailo%20Dataflow%20Compiler-5.4.0-brightgreen.svg :target: https://hailo.ai/company-overview/contact-us/ :alt: Hailo Dataflow Compiler :width: 180 :height: 20
.. |runtime| image:: https://img.shields.io/badge/HailoRT%20(optional)-5.4.0-brightgreen.svg :target: https://hailo.ai/company-overview/contact-us/ :alt: HailoRT :width: 170 :height: 20
.. |license| image:: https://img.shields.io/badge/License-MIT-yellow.svg :target: https://github.com/hailo-ai/hailomodelzoo/blob/master/LICENSE :alt: License: MIT :width: 80 :height: 20
.. image:: docs/images/logo.png
|python| |tensorflow| |cuda| |compiler| |runtime| |license|
The Hailo Model Zoo provides pre-trained models for high-performance deep learning applications. Using the Hailo Model Zoo, you can measure the full precision accuracy of each model, the quantized accuracy using the Hailo Emulator, and measure the accuracy on Hailo's device. Finally, you will be able to generate the Hailo Executable Format (HEF) binary file to speed up development and generate high-quality applications accelerated with Hailo's technology. The Hailo Model Zoo also provides re-training instructions to train the models on custom datasets and models that were trained for specific use cases on internal datasets.
Models Hailo provides different pre-trained models in ONNX / TF formats and pre-compiled HEF (Hailo Executable Format) binary file to execute on the Hailo devices.
The models are divided to:
- Public models - which were trained on publicly available datasets.
Classification <docs/publicmodels/HAILO15H/HAILO15Hclassification.rst>, Object Detection <docs/publicmodels/HAILO15H/HAILO15Hobjectdetection.rst>, Segmentation <docs/publicmodels/HAILO15H/HAILO15Hsemanticsegmentation.rst>, other tasks <docs/PUBLICMODELS.rst>_
* For Hailo-10H - Classification <docs/publicmodels/HAILO10H/HAILO10Hclassification.rst>, Object Detection <docs/publicmodels/HAILO10H/HAILO10Hobjectdetection.rst>, Segmentation <docs/publicmodels/HAILO10H/HAILO10Hsemanticsegmentation.rst>, other tasks <docs/PUBLICMODELS.rst>_
* For Hailo-15L - Classification <docs/publicmodels/HAILO15L/HAILO15Lclassification.rst>, Object Detection <docs/publicmodels/HAILO15L/HAILO15Lobjectdetection.rst>, Segmentation <docs/publicmodels/HAILO15L/HAILO15Lsemanticsegmentation.rst>, other tasks <docs/PUBLICMODELS.rst>_
⚙️ Compatibility Notice: The Hailo-8 and Hailo-8L devices are supported on the Hailo Model Zoo v2.x branch, in combination with the Hailo Dataflow Compiler v3.x branch. The master branch is intended for Hailo-10 and Hailo-15 devices only.
* For Hailo-8 - Classification <docs/publicmodels/HAILO8/HAILO8classification.rst>, Object Detection <docs/publicmodels/HAILO8/HAILO8objectdetection.rst>, Segmentation <docs/publicmodels/HAILO8/HAILO8semanticsegmentation.rst>, other tasks <docs/PUBLICMODELS.rst>_
* For Hailo-8L - Classification <docs/publicmodels/HAILO8L/HAILO8Lclassification.rst>, Object Detection <docs/publicmodels/HAILO8L/HAILO8Lobjectdetection.rst>, Segmentation <docs/publicmodels/HAILO8L/HAILO8Lsemanticsegmentation.rst>, other tasks <docs/PUBLICMODELS.rst>_
- |
HAILO MODELS <docs/HAILOMODELS.rst>which were trained in-house for specific use-cases on internal datasets.
What's new
Core stack updates This release aligns with the latest version of the Hailo software stack, ensuring compatibility and performance improvements.
- Upgraded to Dataflow Compiler v5.4.0
- Upgraded to HailoRT v5.4.0
Hailo also provides RETRAINING INSTRUCTIONS <docs/RETRAINONCUSTOMDATASET.rst> to train a network from the Hailo Model Zoo with custom dataset.
Benchmarks
| List of Hailo's benchmarks can be found in hailo.ai <https://hailo.ai/developer-zone/benchmarks/>_. | In order to reproduce the measurements please refer to the following page <docs/BENCHMARKS.rst>_.
Quick Start Guide
- Install Hailo Dataflow Compiler and enter the virtualenv. In case you are not Hailo customer please contact
hailo.ai <https://hailo.ai/company-overview/contact-us/>_ - Install HailoRT (optional). Required only if you want to run on Hailo's. In case you are not Hailo customer please contact
hailo.ai <https://hailo.ai/company-overview/contact-us/>_ - Clone the Hailo Model Zoo
.. code-block::
git clone https://github.com/hailo-ai/hailomodelzoo.git
- Run the setup script
.. code-block::
cd hailomodelzoo; pip install -e .
- Run the Hailo Model Zoo. For example, print the information of the MobileNet-v1 model:
.. code-block::
hailomz info mobilenet_v1
Getting Started ^^^^^^^^^^^^^^^
For full functionality please see the INSTALLATION GUIDE <docs/GETTINGSTARTED.rst> page (full install instructions and usage examples). The Hailo Model Zoo is using the Hailo Dataflow Compiler for parsing, model optimization, emulation and compilation of the deep learning models. Full functionality includes:
- | Parse: model translation of the input model into Hailo's internal representation.
- | Profiler: generate profiler report of the model. The report contains information about your model and expected performance on the Hailo hardware.
- | Optimize: optimize the deep learning model for inference and generate a numeric translation of the input model into a compressed integer representation.
OPTIMIZATION <docs/OPTIMIZATION.rst>_ page.
- | Compile: run the Hailo compiler to generate the Hailo Executable Format file (HEF) which can be executed on the Hailo hardware.
- | Evaluate: infer the model using the Hailo Emulator or the Hailo hardware and produce the model accuracy.
hailo.ai <https://hailo.ai/company-overview/contact-us/>_.
.. figure:: docs/images/usage_flow.svg
Claude Code Skills
The Hailo Model Zoo ships three Claude Code <https://claude.com/claude-code>_ skills that walk you through the full DFC flow on a fresh model — auto-matching the Model Zoo's parser configs and ALLS optimization scripts when the model name is known, and falling back to interactive guidance when it is not:
- `
/hailo-parse <path-to-model> [hwarch]— parses an ONNX or TensorFlow model into a Hailo HAR, looking up start/end nodes fromcfg/networks/when available. /hailo-optimize <parsed.har> [hwarch]— applies a model script and quantizes the network, auto-loading a matching ALLS fromcfg/alls/, validating calibration-set normalization, using GPU when available, and gating on the post-optimize SNR./hailo-compile <optimized.har> [hw_arch]— compiles the optimized HAR into a deployable HEF and provides triage for common compilation failures.
.claude/skills/). They use only the public hailosdkclient API from the DFC wheel, so the DFC remains a black box. See the DFC User Guide on the developer-zone _ for the full DFC reference.
License
The Hailo Model Zoo is released under the MIT license. Please see the
LICENSE _ file for more information.
Support & Issues
⚠️ Issue reporting is disabled in this repository.
For bug reports, feature requests, or discussions, please visit our
Hailo Community Forum _.
Changelog
For further information please see our
CHANGELOG 
