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4.1 Demo Zoo Overview

Last Version: 11/09/2025

Overview​

The Bianbu AI Demo Zoo is a collection of sample projects developed by SpacemiT. It provides reference implementations for deploying various deep learning models on K1 series chips. THis demonstrates an end-to-end inference workflow.

The project is organized into two main branches:

  • Computer Vision (CV): Covers tasks like image classification, object detection, and face recognition.
  • Natural Language Processing (NLP): Supports typical NLP tasks (see repository for details).

Both branches support C++ and Python, making them suitable for real-world deployment scenarios.

  • Project repository: ⭐ Bianbu AI Demo Zoo

  • Supported models: For details, see the full Model List. Currently, it covers common models such as

    • Classification: ResNet, MobileNet
    • Detection: YOLOv5, YOLOX
    • Face Recognition: ArcFace

Demos​

Most models provide inference examples in both Python and C++. After downloading the required model weights and test data (see README.md for step-by-step instructions).

Python Demo​

Taking ResNet image classification as an example:

cd python
python test_resnet.py  # Default to using the ResNet50 model.

After the model finishes running, it will output the predicted class labels.

C++ Demo​

cd cpp
mkdir build
cd build
cmake ..
make -j8
./resnet_demo --model /path/to/resnet50.onnx --image /path/to/image.jpg

After running, the prediction results will also be output in the terminal. You can combine this with OpenCV to render the classification labels.