I got different result between onnx and ncnn model even if input is a zeros mat. (Already checked the FAQ)
model.zip
Password:0000
Here's the onnx and ncnn model. (You can also try to regenerate ncnn model from onnx)
@nihui

A simple test:

#include "net.h"

int main(int argc, char** argv)
{
    ncnn::UnlockedPoolAllocator ncnn_blob_pool_allocator_;
    ncnn::PoolAllocator ncnn_workspace_pool_allocator_;
    ncnn::Net ncnn_detector_;

    ncnn::Option opt;
    opt.blob_allocator = &ncnn_blob_pool_allocator_;
    opt.workspace_allocator = &ncnn_workspace_pool_allocator_;

    ncnn_detector_.opt = opt;
    ncnn_detector_.load_param("model-opt.param");
    ncnn_detector_.load_model("model-opt.bin");

    cv::Mat ncnn_cv_mat(56, 56, CV_32FC3, cv::Scalar(0, 0, 0));
    ncnn::Mat ncnn_in = ncnn::Mat::from_pixels(ncnn_cv_mat.data, ncnn::Mat::PIXEL_BGR, ncnn_cv_mat.cols, ncnn_cv_mat.rows);

    auto ncnn_extractor = ncnn_detector_.create_extractor();
    ncnn_extractor.input("main_input", in);

    ncnn::Mat out;
    ncnn_extractor.extract("class_ret", out);

    std::vector<float> cls_scores;
    cls_scores.resize(ncnn_out.w);
    for (int j = 0; j < ncnn_out.w; j++)
    {
        cls_scores[j] = ncnn_out[j];
    }

    return 0;
}

NCNN output: 0.43, -0.66

import onnxruntime as ort
import numpy as np
from PIL import Image

img = Image.new("RGB", (56, 56), "black")
inputs = np.array(img, dtype=np.float32)
inputs = inputs.transpose(2, 0, 1)
inputs = np.expand_dims(inputs, axis=0)

ort_sess = ort.InferenceSession('model.onnx')
outputs = ort_sess.run(None, {'main_input': inputs })
print(outputs)

ONNX(and other transform like tflite): -0.05 , -0.09

on win10 x64, tested every version from 2020.12 till latest version of ncnn