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Hailo Model Zoo


Python 3.8 Generic badge Generic badge Generic badge Generic badge License: MIT

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 the Hailo-8 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-8. 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.


Hailo provides different pre-trained models in ONNX / TF formats and pre-compiled HEF (Hailo Executable Format) binary file to execute on the Hailo-8 device.
The models are divided to:
  • PUBLIC MODELS which were trained on publicly available datasets.
  • HAILO MODELS which were trained in-house for specific use-cases on internal datasets.
    Each Hailo Model is accompanied with retraining instructions.


Hailo also provides RETRAINING INSTRUCTIONS to train a network from the Hailo Model Zoo with custom dataset.


List of Hailo's benchmarks can be found in
In order to reproduce the measurements please refer to the following page.

Quick Start Guide

  • Install Hailo Dataflow Compiler and enter the virtualenv. In case you are not Hailo customer please contact

  • Install HailoRT (optional). Required only if you want to run on Hailo-8. In case you are not Hailo customer please contact

  • Clone the Hailo Model Zoo

    git clone
  • Run the setup script

    cd hailo_model_zoo; pip install -e .
  • Run the Hailo Model Zoo. For example, print the information of the MobileNet-v1 model:

    hailomz info mobilenet_v1

Getting Started

For full functionality please see the INSTALLATION GUIDE 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.
    For further information please see our OPTIMIZATION 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.

For further information about the Hailo Dataflow Compiler please contact


The Hailo Model Zoo is released under the MIT license. Please see the LICENSE file for more information.


Please visit for support / requests / issues.


For further information please see our CHANGLOGE page.