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Image Classification - ResNet18

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

This example demonstrates how to deploy the ResNet18 model for image classification inference using C/C++. It is intended for RDK S600 devices equipped with a BPU chip. The sample code is located in /app/cdev_demo/bpu/classification_sample/resnet18/.

Model Description​

  • Overview:

    ResNet (Residual Network) is a deep convolutional neural network architecture proposed by Microsoft Research. Its core idea is to introduce "residual connections," which use skip connections across layers to mitigate the vanishing gradient problem in deep networks, enabling effective training of networks with dozens or even hundreds of layers. The ResNet18 variant used in this example is a lightweight version with 18 layers, widely used for image classification, feature extraction, and similar tasks.

  • HBM model name: resnet18_224x224_nv12.hbm

  • Input format: NV12, size 224x224

  • Output: Softmax probability distribution over 1000 classes

Feature Overview​

  • Model loading

    Load the specified model and parse input/output names and shapes for subsequent inference.

  • Input preprocessing

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

  • Inference execution

    Perform the forward pass via the .infer() method.

  • Result post-processing

    Read the output tensor, parse the top-K classification results (Top-5), and display class labels and probability values.

Environment Dependencies​

Before building 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
|   `-- resnet18.hpp       # ResNet18 model inference class definition
`-- src/                   # Source code
    |-- main.cc            # Program entry point; invokes the ResNet18 inference pipeline
    `-- resnet18.cc        # ResNet18 inference class implementation

Build the Project​

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

Model Download​

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

wget https://archive.d-robotics.cc/downloads/rdk_model_zoo/rdk_s100/ResNet/resnet18_224x224_nv12.hbm
wget https://archive.d-robotics.cc/downloads/rdk_model_zoo/rdk_s600/ResNet/resnet18_224x224_nv12.hbm

Parameter Reference​

ParameterDescriptionDefault Value
--model_pathModel file path (.hbm format)/opt/hobot/model/s100/basic/resnet18_224x224_nv12.hbm
--test_imgTest image path/app/res/assets/zebra_cls.jpg
--label_fileImageNet class mapping (dict, one index\tlabel per line)/app/res/labels/imagenet1000_clsidx_to_labels.txt
--top_kNumber of top-K classification results to output5
ParameterDescriptionDefault Value
--model_pathModel file path (.hbm format)/opt/hobot/model/s600/basic/resnet18_224x224_nv12.hbm
--test_imgTest image path/app/res/assets/zebra_cls.jpg
--label_fileImageNet class mapping (dict, one index\tlabel per line)/app/res/labels/imagenet1000_clsidx_to_labels.txt
--top_kNumber of top-K classification results to output5

Quick Start​

  • Run the model

    • Make sure you are in the build directory

    • Use 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
      ./resnet_18 \
      --model_path /opt/hobot/model/s600/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 the 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 shows the top-K classes with the highest probabilities.

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

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