PyeIQ v1.0 for BSP 5.4.3_2.0.0 Release 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 This video is currently being processed. Please try again in a few minutes. (view in My Videos) 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 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 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 Re: PyeIQ v1.0 for BSP 5.4.3_2.0.0 Release Hello
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