Seeed SenseCraft Model Assistant (or simply SSCMA) is an open-source project focused on embedded AI. We have optimized excellent algorithms from OpenMMLab for real-world scenarios and made implementation more user-friendly, achieving faster and more accurate inference on embedded devices.
Currently we support the following directions of algorithms:
🔍 Anomaly Detection
In the real world, anomalous data is often difficult to identify, and even if it can be identified, it requires a very high cost. The anomaly detection algorithm collects normal data in a low-cost way, and anything outside normal data is considered anomalous.
👁️ Computer Vision
Here we provide a number of computer vision algorithms such as object detection, image classification, image segmentation and pose estimation. However, these algorithms cannot run on low-cost hardware. SSCMA optimizes these computer vision algorithms to achieve good running speed and accuracy in low-end devices.
⏱️ Scenario Specific
SSCMA provides customized scenarios for specific production environments, such as identification of analog instruments, traditional digital meters, and audio classification. We will continue to add more algorithms for specified scenarios in the future.
SSCMA provides a user-friendly platform that allows users to easily perform training on collected data, and to better understand the performance of algorithms through visualizations generated during the training process.
🔋 Models with low computing power and high performance
SSCMA focuses on end-side AI algorithm research, and the algorithm models can be deployed on microprocessors, similar to ESP32, some Arduino development boards, and even in embedded SBCs such as Raspberry Pi.
🗂️ Supports multiple formats for model export
TensorFlow Lite is mainly used in microcontrollers, while ONNX is mainly used in devices with Embedded Linux. There are some special formats such as TensorRT, OpenVINO which are already well supported by OpenMMLab. SSCMA has added TFLite model export for microcontrollers, which can be directly converted to TensorRT, UF2 format and drag-and-drop into the device for deployment.
Pointer Meter Recognition
Digital Meter Recognition
More application examples can be found in Model Zoo。