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.. 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 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.
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.
For Hailo-8 - Classification <docs/public_models/HAILO8/HAILO8_classification.rst>
_, Object Detection <docs/public_models/HAILO8/HAILO8_object_detection.rst>
_, Segmentation <docs/public_models/HAILO8/HAILO8_semantic_segmentation.rst>
_, other tasks <docs/PUBLIC_MODELS.rst>
_
For Hailo-8L - Classification <docs/public_models/HAILO8L/HAILO8l_classification.rst>
_, Object Detection <docs/public_models/HAILO8L/HAILO8l_object_detection.rst>
_, Segmentation <docs/public_models/HAILO8L/HAILO8l_semantic_segmentation.rst>
_, other tasks <docs/PUBLIC_MODELS.rst>
_
For Hailo-15H - Classification <docs/public_models/HAILO15H/HAILO15H_classification.rst>
_, Object Detection <docs/public_models/HAILO15H/HAILO15H_object_detection.rst>
_, Segmentation <docs/public_models/HAILO15H/HAILO15H_semantic_segmentation.rst>
_, other tasks <docs/PUBLIC_MODELS.rst>
_
For Hailo-15M - Classification <docs/public_models/HAILO15M/HAILO15M_classification.rst>
_, Object Detection <docs/public_models/HAILO15M/HAILO15M_object_detection.rst>
_, Segmentation <docs/public_models/HAILO15M/HAILO15M_semantic_segmentation.rst>
_, other tasks <docs/PUBLIC_MODELS.rst>
_
| HAILO MODELS <docs/HAILO_MODELS.rst>
_ 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 <docs/RETRAIN_ON_CUSTOM_DATASET.rst>
_ to train a network from the Hailo Model Zoo with custom dataset.
| 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>
.
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-8. 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::
Run the setup script
.. code-block::
cd hailo_model_zoo; 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/GETTING_STARTED.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:
OPTIMIZATION <docs/OPTIMIZATION.rst>
_ page.For further information about the Hailo Dataflow Compiler please contact hailo.ai <https://hailo.ai/company-overview/contact-us/>
_.
.. figure:: docs/images/usage_flow.svg
The Hailo Model Zoo is released under the MIT license. Please see the LICENSE <https://github.com/hailo-ai/hailo_model_zoo/blob/master/LICENSE>
_ file for more information.
Please visit hailo.ai <https://hailo.ai/>
_ for support / requests / issues.
For further information please see our CHANGELOG <docs/CHANGELOG.rst>
_ page.