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
- Hailo-8 or Hailo-10 device
- HailoRT PCIe driver installed
- At least 6GB's of free disk space
Note
This version is compatible with HailoRT v4.22.0 for Hailo-8 devices, and with HailoRT v5.0.0 for Hailo-10 devices.
| 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
- 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
TAPPAS is now released separately for Hailo-8, 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.
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.
Important
The models provided when installing from GitHub are for Hailo-8 devices.
For Hailo-10H devices, please download the models from the
Hailo Model Zoo and place them in the apps/h8/gstreamer/general/detection/resources/ directory:
- yolov8m
- ssd_mobilenet_v1
- nanodet
- There specific compilation of yolov5m (yolov5m_wo_spp_60p.hef) provided for this application, isn't provided for Hailo-10H devices.
Note
Running application examples requires a direct connection to a monitor.
If you need support, please post your question on our Hailo community Forum for assistance.
Contact information is available at hailo.ai.
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-10)
- Known issue: When installing via GitHub, only Hailo-8 models are downloaded.