Plumerai Documentation Overview¶
On these pages you can find documentation for Plumerai's products. For information about the company itself, visit our main website or our blog to find out what is going on at Plumerai.
Plumerai People Detection¶
At Plumerai we've built the world’s most accurate people detection for the edge. We deliver a complete software solution for camera-based people detection. Our models are tiny and fast and detect people with a high accuracy in any situation. For more information about the product, see our people detection page.
There's a demo that runs live in your browser. Try it here!
Technical documentation:
- Plumerai People Detection for Arm Cortex-A and x86
- Plumerai People Detection for microcontrollers, e.g. Arm Cortex-M.
- Plumerai People Detection demo on Arm and x86 systems
- Plumerai People Detection demo on the ESP32-S3
Additionally, we provide example usage of the Plumerai People Detection library in the form of a simple OpenCV webcam-to-display pipeline. Documentation and source code is available in a GitHub repository.
Plumerai Familiar Face Identification¶
Familiar face identification identifies individuals based by comparing them to a limited collection of recognized faces stored in the system. To enroll in the system, a few images from different angles of their face are captured. The Plumerai Familiar Face Identification software solution is accurate, fast, and runs on Arm Cortex-A and x86 CPUs.
There's a demo that runs live in your browser. Try it here!
These pages provide the technical documentation for our people detection products:
Technical documentation:
Plumerai Inference Engine¶
We've also built the world’s fastest deep learning inference engine for Arm Cortex-M and other microcontrollers. You can find out more about the inference engine on our blog or try it out with your own model.
The technical documentation of can be found on different pages grouped per topic:
- Overview of the inference engine: Overview of the supported input model formats.
- Model format support: Overview of the supported input model formats.
- Layer and op support: Lists which neural network layers/ops are supported.
- Reporting and analysis: Information about the reporting and analysis options.
- Correctness validation: Details on how to run in validation mode to ensure correctness.
- Building and integration: Instructions on how to build and use the C++ API or C API.
- Example models: LeNet, MobileNetV2, an LSTM and a GRU-based RNN.