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Image Classification - ResNet18

This example demonstrates how to deploy the ResNet18 model using C/C++ for image classification inference on RDK S100 devices equipped with a BPU chip. The example code is located in the /app/cdev_demo/bpu/01_classification_sample/01_resnet18/ directory.

Model Description​

  • Introduction:

    ResNet (Residual Network) is a deep convolutional neural network architecture proposed by Microsoft Research. Its core idea is the introduction of "residual connections," which alleviate the vanishing gradient problem in deep networks through shortcut connections across layers, enabling effective training of networks with dozens or even hundreds of layers. The ResNet18 used in this example is a lightweight variant with an 18-layer structure, widely applied in tasks such as image classification and feature extraction.

  • HBM Model Name: resnet18_224x224_nv12.hbm

  • Input Format: NV12, size 224x224

  • Output: Softmax probability distribution over 1000 classes

Functionality Overview​

  • Model Loading

    Loads the specified model and parses input/output names and shapes for subsequent inference.

  • Input Preprocessing

    Resizes the BGR image to 224x224 and converts it to NV12 format (separated Y and UV planes).

  • Inference Execution

    Performs forward inference using the .infer() method.

  • Result Post-processing

    Reads the output tensor, parses the top-K classification results (Top-5), and displays class labels along with their probabilities.

Environment Dependencies​

Before compiling and running, ensure the following dependencies are installed:

sudo apt update
sudo apt install libgflags-dev

Directory Structure​

.
|-- CMakeLists.txt         # CMake build script
|-- README.md              # Project documentation
|-- inc/                   # Header files directory
|   `-- resnet18.hpp       # Definition of ResNet18 inference class
`-- src/                   # Source code directory
    |-- main.cc            # Program entry point, invoking ResNet18 inference pipeline
    `-- resnet18.cc        # Implementation of ResNet18 inference class

Build Instructions​

  • Configuration and Compilation 
    mkdir build && cd build
    cmake ..
    make -j$(nproc)

Model Download​

If the model is not found at runtime, download it using the following command:

wget https://archive.d-robotics.cc/downloads/rdk_model_zoo/rdk_s100/ResNet/resnet18_224x224_nv12.hbm

Parameter Description​

ParameterDescriptionDefault Value
--model_pathPath to the model file (.hbm format)/opt/hobot/model/s100/basic/resnet18_224x224_nv12.hbm
--test_imgPath to the test image/app/res/assets/zebra_cls.jpg
--label_fileImageNet class mapping (dictionary, each line: index\tlabel)/app/res/labels/imagenet1000_clsidx_to_labels.txt
--top_kNumber of top-K classification results to output5

Quick Start​

  • Run the Model

    • Ensure you are in the build directory.
    • Run with default parameters: 
      ./resnet_18
    • Run with custom parameters: 
      ./resnet_18 \
      --model_path /opt/hobot/model/s100/basic/resnet18_224x224_nv12.hbm \
      --test_img   /app/res/assets/zebra_cls.jpg \
      --label_file /app/res/labels/imagenet1000_clsidx_to_labels.txt \
      --top_k 5

  • View Results

    Top-5 Predictions:
    zebra: 0.9979
    impala, Aepyceros melampus: 0.0005
    cheetah, chetah, Acinonyx jubatus: 0.0005
    gazelle: 0.0004
    prairie chicken, prairie grouse, prairie fowl: 0.0002

Notes​

  • The output displays the top-K classes with the highest probabilities.

  • For more information on deployment methods or model support, please refer to the official documentation or contact platform technical support.

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