Welcome to PyeIQ
PyeIQ gathers everything needed by itself. It provides a simplified way to run ML applications, which avoids the user spending time on preparing the environment.
| PyeIQ Version | Release Date | Notes |
|---|---|---|
| tag_v1.0 | Apr 29, 2020 | - |
| tag_v2.0 | - | Planned for June |
| i.MX Board | BSP Release | Building Status |
|---|---|---|
| 8 QM | 5.4.3_2.0.0 | passing |
| 8 MPlus | 5.4.3_2.0.0 | passing |
Getting Started with PyeIQ
1. Easy Installation
- If you prefer to build the package by yourself go to Appendix Section or follow the README file at the PyeIQ repo.
1.1 Copy the PyeIQ pre-built package attached to the board, and then install it by using pip3 tool:
1.2 Check the installation by starting an interactive shell:
1.3 Import PyeIQ and see the version:
The output is the PyeIQ latest version installed in the system.- (Optional) Install the following package to show downloading status:
2. Easy Running
All demos and applications are automatically installed in /opt/eiq.2.1 To run the demos:
2.2 To run the applications:
2.3 Use help if needed:
3. List of Available Demos and Applications
| Demo/App Name | Demo/App Type | i.MX Board | BSP Release | BSP Framework | Inference | Status | Notes |
|---|---|---|---|---|---|---|---|
| Label Image | File Based | QM, MPlus | 5.4.3_2.0.0 | TensorFlow Lite 2.1.0 | GPU, NPU | passing | - |
| Label Image Switch | File Based | QM, MPlus | 5.4.3_2.0.0 | TensorFlow Lite 2.1.0 | GPU, NPU | passing | - |
| Object Detection | SSD/Camera | QM, MPlus | 5.4.3_2.0.0 | TensorFlow Lite 2.1.0 | GPU, NPU | passing | Need better model. |
| Object Detection OpenCV | SSD/Camera | QM, MPlus | 5.4.3_2.0.0 | TensorFlow Lite 2.1.0 | GPU, NPU | passing | Need better model. |
| Object Detection N. GS. | SSD/Camera | QM, MPlus | 5.4.3_2.0.0 | TensorFlow Lite 2.1.0 | GPU, NPU | - | Pending issues. |
| Object Detection Yolov3 | SSD/File | QM, MPlus | 5.4.3_2.0.0 | TensorFlow Lite 2.1.0 | GPU, NPU | - | Pending issues. |
| Object Detection Yolov3 | SSD/Camera | QM, MPlus | 5.4.3_2.0.0 | TensorFlow Lite 2.1.0 | GPU, NPU | - | Pending issues. |
| Fire Detection | File Based | QM, MPlus | 5.4.3_2.0.0 | TensorFlow Lite 2.1.0 | GPU, NPU | passing | - |
| Fire Detection | Camera | QM, MPlus | 5.4.3_2.0.0 | TensorFlow Lite 2.1.0 | GPU, NPU | passing | - |
| Fire Detection | Camera | - | 5.4.3_2.0.0 | PyArmNN 19.08 | - | - | Requires 19.11 |
| Coral Posenet | Camera | - | - | - | - | - | Ongoing |
| NEO DLR | Camera | - | - | - | - | - | Ongoing |
4. Examples
4.1 Fire Detection Image
4.1.1 Non-fire
Running Fire Detect Image:
Output:
INFO: Created TensorFlow Lite delegate for NNAPI.
Applied NNAPI delegate.
Inference time: 0:00:00.264853
Non-Fire
Applied NNAPI delegate.
Inference time: 0:00:00.264853
Non-Fire
4.1.2 Fire
Running Fire Detect Image:
Output:
INFO: Created TensorFlow Lite delegate for NNAPI.
Applied NNAPI delegate.
Inference time: 0:00:00.193055
Fire
Applied NNAPI delegate.
Inference time: 0:00:00.193055
Fire
4.2 Fire Detection Camera
Running Fire Detect Camera:
Output:
PyeIQ also supports training for Fire Detection demo, please refer to PyeIQ - Training and Conversion Support (Keras/TensorFlow Lite) .
4.3 Label Image Switch
Running Switch Label Image:
Output:
Cores Comparison (CPU, GPU and NPU)
- Check the following graphical plot for Switch Label Image demo:
- Check the following graphical plot for the other demos:
We are currently working to reduce the inference time on Fire Detection demos.
Appendix Section
The procedures described in this document target a GNU/Linux Distribution Ubuntu 18.04.
1. Software Requirements
1.1 Install the following packages in the GNU/Linux system:
1.2 Then, use pip3 tool to install the virtualenv tool:
2. Building the PyeIQ Package
2.1 Clone the repository:
2.2 Use virtualenv tool to create an isolated Python environment:
2.3 Generate the PyeIQ package:
2.4 Copy the package to the board:
2.5 To deactivate the virtual environment:
Contact
Feel free to contact us about any issue/bug you might have it. Your feedback is very welcome so we can improve the next version 
- Alifer Moraes
- diegodorta
- marcofranchi
