Repository: opencv
Issue: Int8 Quantized model same output on different inputs
Labels: bug

### System Information

OpenCV python version: 4.7.0.72
Operating System / Platform: Windows 10
Python version: 3.10.6

### Detailed description

I am trying to run an int8 quantized model exported to ONNX with opencv backend.

The **problem** is with the output of the model. Running inference on **different** images gives the **same** output tensor.

I tried the inference with pytorch (quantized .pth model) and with openvino (same onnx file) and they give good results.


OpenCV output:
![opencv_output_tensor](https://user-images.githubusercontent.com/69692905/223679691-bfb8bb75-c454-447c-9c6b-376802e656bf.PNG)
OpenVino output:
![openvino_out_tensor](https://user-images.githubusercontent.com/69692905/223679826-d499c60c-bfbe-4c96-916e-928a8e8e6ae4.PNG)


### Steps to reproduce

Note: This is not a fully trained model, so it should not return the correct label!

The model:
[int8_quantized.zip](https://github.com/opencv/opencv/files/10919343/int8_quantized.zip)
Test images:
[test.zip](https://github.com/opencv/opencv/files/10919466/test.zip)


```
import openvino.runtime as vino
import cv2
import argparse
import numpy as np
import random
import time

def postprocess(output_blob,_labels):
        _labels=['beige', 'black', 'blue', 'brown', 'golden', 'green', 'grey', 'red', 'silver', 'white']
        batched_class_id_list = []
        for o in output_blob:
            #o=softmax(o)
            class_id_list = o.argsort()[::-1][:1]
            batched_class_id_list.append(class_id_list)
        if len(_labels) > 0:
            batched_predicted_labels = []
            for class_id_list in batched_class_id_list:
                predicted_labels = []
                for class_id in class_id_list:
                    predicted_labels.append(_labels[class_id])
                batched_predicted_labels.append(predicted_labels)
            return batched_predicted_labels
        else:
            return batched_class_id_list

ie = vino.Core()
model = ie.read_model(r"model_path.onnx")
cv_model = cv2.dnn.readNet(r"model_path.onnx")

compiled_model = ie.compile_model(model=model, device_name="CPU")
input_layer = compiled_model.input(0)
img_size = (112, 112) 

src_img = cv2.imread(r"img_path.jpg")
src_size = src_img.shape[:2]

img = cv2.resize(src_img,img_size, interpolation=cv2.INTER_LINEAR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = img.astype(dtype=np.float32)
img /= 255.0
input_image = np.expand_dims(img.transpose(2, 0, 1), 0)
cv_model.setInput(input_image)
cv_outputs = cv_model.forward()
cv_pred=postprocess(cv_outputs,None)
pred = compiled_model([input_image])

print("OPENCV OUTPUT TENSOR:")
print(cv_outputs)
print("OPENVINO OUTPUT TENSOR:")
print(pred)
tmp_out=pred[compiled_model.output(0)]

#print(postprocess(tmp_out,None))



```
![BOTH_OUTPUT](https://user-images.githubusercontent.com/69692905/223683884-a97961ce-e939-4622-9d43-34b564d63ea5.PNG)


### Issue submission checklist

- [X] I report the issue, it's not a question
- [X] I checked the problem with documentation, FAQ, open issues, forum.opencv.org, Stack Overflow, etc and have not found any solution
- [X] I updated to the latest OpenCV version and the issue is still there
- [X] There is reproducer code and related data files (videos, images, onnx, etc)