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https://github.com/hailo-ai/tappas

High-performance, optimized pre-trained template AI application pipelines for systems using Hailo devices
https://github.com/hailo-ai/tappas

deep-learning edge-ai gstreamer hardware-acceleration machine-learning neural-networks video-processing

Last synced: 6 months ago
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High-performance, optimized pre-trained template AI application pipelines for systems using Hailo devices

Awesome Lists containing this project

README 

          
Hailo TAPPAS - Optimized Execution of Video-Processing Pipelines

================================================================



.. |gstreamer| image:: https://img.shields.io/badge/gstreamer-1.16%20%7C%201.18%20%7C%201.20-blue

   :target: https://gstreamer.freedesktop.org/

   :alt: Gstreamer 1.16 | 1.18 | 1.20

   :width: 150

   :height: 20



.. |hailort| image:: https://img.shields.io/badge/HailoRT-4.23.0%20%7C%205.2.0-green

   :target: https://github.com/hailo-ai/hailort

   :alt: HailoRT 4.23.0 | 5.2.0

   :height: 20



.. |license| image:: https://img.shields.io/badge/License-LGPLv2.1-green

   :target: https://github.com/hailo-ai/tappas/blob/master/LICENSE

   :alt: License: LGPL v2.1

   :height: 20



.. |check_mark| image:: ./resources/check_mark.png

  :width: 20

  :align: middle



.. image:: ./resources/github_Tappas_Mar24.jpg

  :height: 300

  :width: 600

  :align: center



|gstreamer| |hailort| |license|



----



Overview

--------



TAPPAS is Hailo's infrasturcture for building applications, implementing pipeline elements and

pre-trained AI tasks.



Hailo apllications are now maintained at `this repository `_.



Demonstrating Hailo's system integration scenario of specific use cases on predefined systems

(software and Hardware platforms). It can be used for evaluations, reference code and demos:



* Accelerating time to market by reducing development time and deployment effort

* Simplifying integration with Hailo’s runtime SW stack

* Providing a starting point for customers to fine-tune their applications



----



Getting Started with Hailo-8 And Hailo-10H

-----------------------------------------



Prerequisites

^^^^^^^^^^^^^



* Hailo-8 or Hailo-10H device

* HailoRT PCIe driver installed

* At least 6GB's of free disk space



.. note::

    This version is compatible with HailoRT v4.23.0 for Hailo-8 devices, and with HailoRT v5.2.0 for Hailo-10H devices.



Installation

^^^^^^^^^^^^



.. list-table::

   :header-rows: 1



   * - Option

     - Instructions

     - Supported OS

   * - **Hailo SW Suite***

     - `SW Suite Install guide `_

     - Ubuntu x86 24.04, Ubuntu x86 22.04

   * - Manual install

     - `Manual install guide `_

     - Ubuntu x86 24.04, Ubuntu x86 22.04, Ubuntu aarch64 20.04

   * - Yocto installation

     - `Read more about Yocto installation `_

     - Yocto supported BSP's

   * - Raspberry Pi 5 installation

     - `Read more about Raspberry Pi 5 installation `_

     - Raspberry Pi OS



``* It is recommended to start your development journey by first installing the Hailo SW Suite``



Documentation

^^^^^^^^^^^^^



* `Framework architecture and elements documentation `_

* `Guide to writing your own C++ postprocess element `_

* `Guide to writing your own Python postprocess element `_

* `Debugging and profiling performance `_

* `Cross compile `_ - A guide for cross-compiling



----



Getting Started with Hailo-15

-----------------------------



TAPPAS is now released separately for Hailo-8 and Hailo-10H, for Hailo-15 please refer to https://github.com/hailo-ai/hailo-camera-apps.



For a quick start with Hailo-15, please refer to the Vision Processor Software Package documentation section

in Hailo's `Developer Zone `_.



----



Example Applications Built with TAPPAS

--------------------------------------



TAPPAS includes a `single-stream object detection pipeline `_ built on top of GStreamer.

These example application is part of the Hailo AI Software Suite.



Hailo offers an additional set of

`Application Code Examples `_.

For the Raspberry Pi 5 applications, go to

`Hailo Raspberry Pi 5 Examples `_.



.. important:: 

    * Example application utilize both the host (for non-neural tasks) and the Neural-Network Core

      (for neural-networks inference), therefore performance results are affected by the host.

    * This application example does not include any architecture-specific accelerator usage,

      and therefore will provide the easiest way to run an application, but with sub-optimal performance.



.. note::

    Running application examples requires a direct connection to a monitor.



----



Support

-------



If you need support, please post your question on our `Hailo community Forum `_ for assistance.



Contact information is available at `hailo.ai `_.



----



Changelog

----------



**v5.2.0 (December 2025)**



* Added ``--static-opencv`` option to ``install.sh`` for static OpenCV linking.

* Added Python wheel builder for TAPPAS Python binding package

* Updated pybind11 to support NumPy 2.x

* Removed Hailo-15 support and related code

* This release supports both HailoRT v4.23.0 (Hailo-8) and HailoRT v5.2.0 (Hailo-10H)



**v5.1.0 (October 2025)**



* Downloader: removed redundant CLI arguments (``--platform``, ``--app-list``);

* Downloader: HEF files now downloaded from ``model_zoo`` and media files from the TAPPAS bucket; removed the uploader;

* Detection app: ``detection.sh`` now supports ``--arch`` (Hailo-8/Hailo-10H);

* Models and resources: migrated model files to ``model_zoo``; TAPPAS bucket is now used only for general MP4 files; updated resources directory structure; changed ``yolov5m_wo_spp_60p.hef`` to ``yolov5m_wo_spp.hef``.

* Hailo‑10H support: added Hailo-10H HEF downloads.

* Build and packaging: separated GCC apt installation and removed fixed GCC version; updated related documentation.

* Dependencies: updated package versions for Python 3.13 compatibility; migrated pandas to support the newer environment.

* Cleanup: removed Hailo‑8 references where appropriate; removed nested directories under apps; various comment updates.

* This release supports both HailoRT v4.23.0 (Hailo-8) and HailoRT v5.1.0 (Hailo-10H)



**v5.0.0 (July 2025)**



* All example applications, except the object detection application, are now maintained at `Hailo Applications `_.

* Updated manual installation process

* Added support for Ubuntu 24.04

* Added support for Python 3.12

* This release supports both HailoRT v4.22.0 (Hailo-8) and HailoRT v5.0.0 (Hailo-10H)

* Known issue: When installing via GitHub, only Hailo-8 models are downloaded.