Resnet Quickstart

Field	Value
Difficulty	Intermediate
Estimated Read Time	10-15 minutes
Labels	resnet, classification, mpk

Concept

This tutorial is a quick classifier bring-up path using a ResNet-style MPK.

If YOLO quickstart teaches detection flow, this chapter teaches classification flow. It helps new users verify model loading, preprocessing assumptions, and output tensor shape before building application logic on top.

What this chapter demonstrates:

• Loading a ResNet MPK with explicit image/preproc options.
• Running a deterministic classification inference path.
• Inspecting output tensor rank and leading dimension as a sanity check.

Use-case guidance:

• First classification model integration in a new environment.
• Validating normalization/format settings for classifier accuracy.
• Establishing baseline output contracts before label mapping/top-k handling.

Reference:

• Model
• Model Options

Learning Process

1. Resolve ResNet MPK and deterministic classifier input tensor/image.
2. Configure model input/preproc options for classification.
3. Run inference and inspect output tensor rank/shape cues.
4. Confirm expected path with CHECK, SIGNATURE, and [OK].

Run

NEAT_EXTRAS_ROOT=<sima-neat-*-Linux-extras>
cd $NEAT_EXTRAS_ROOT/lib/sima-neat/tutorials
./tutorial_v2_013_resnet_quickstart --mpk /path/to/model.tar.gz

Code

tutorials/013_resnet_quickstart/resnet_quickstart.cpp

// ResNet quickstart: ImageNet normalization via Model::Options, then one-shot model.run.
//
// Usage:
//   tutorial_v2_013_resnet_quickstart --mpk /path/to/resnet_50.tar.gz --image /path/to.jpg

#include "neat.h"

#include <opencv2/imgcodecs.hpp>
#include <opencv2/imgproc.hpp>

#include <array>
#include <iostream>
#include <stdexcept>
#include <string>

namespace {

bool get_arg(int argc, char** argv, const std::string& key, std::string& out) {
  for (int i = 1; i + 1 < argc; ++i) {
    if (key == argv[i]) {
      out = argv[i + 1];
      return true;
    }
  }
  return false;
}

} // namespace

int main(int argc, char** argv) {
  try {
    std::string mpk, image;
    if (!get_arg(argc, argv, "--mpk", mpk) || !get_arg(argc, argv, "--image", image)) {
      std::cerr << "Usage: tutorial_v2_013_resnet_quickstart --mpk <path> --image <path>\n";
      return 1;
    }

    cv::Mat bgr = cv::imread(image, cv::IMREAD_COLOR);
    if (bgr.empty())
      throw std::runtime_error("failed to load image: " + image);

    cv::Mat rgb;
    cv::cvtColor(bgr, rgb, cv::COLOR_BGR2RGB);
    cv::resize(rgb, rgb, cv::Size(224, 224));
    if (!rgb.isContinuous())
      rgb = rgb.clone();

    // CORE LOGIC
    // ImageNet-trained ResNet needs per-channel mean/stddev normalization.
    simaai::neat::Model::Options opt;
    opt.media_type = "video/x-raw";
    opt.format = "RGB";
    opt.input_max_width = 224;
    opt.input_max_height = 224;
    opt.input_max_depth = 3;
    opt.preproc.normalize = true;
    opt.preproc.channel_mean = std::array<float, 3>{0.485f, 0.456f, 0.406f};
    opt.preproc.channel_stddev = std::array<float, 3>{0.229f, 0.224f, 0.225f};

    simaai::neat::Model model(mpk, opt);
    auto out = model.run(rgb, /*timeout_ms=*/2000);

    if (!out.tensor.has_value())
      throw std::runtime_error("missing output tensor");
    std::cout << "output_rank=" << out.tensor->shape.size() << "\n";
    if (!out.tensor->shape.empty())
      std::cout << "first_dim=" << out.tensor->shape.front() << "\n";
    std::cout << "[OK] 013_resnet_quickstart\n";
    return 0;
  } catch (const std::exception& e) {
    std::cerr << "[FAIL] " << e.what() << "\n";
    return 1;
  }
}

Source

• C++
• Python
• README