Inference issue with Quantized yolov8 OpenVINO model

[chandra-ps612]

Hello @MaximProshin,
Recently, I'm doing post-training quantization using NNCF with yolov8_custom_model(Uncompressed OpenVINO model). I observed that It is generating similar inference results on test set, Albeit its quantized version is unable to do inference on the same dataset.

Creating and instantiating CustomDataset Class

from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import openvino as ov

class CustomDataset(Dataset):
    def __init__(self, images_dir, labels_dir, transform=None):
        self.images_dir = images_dir
        self.labels_dir = labels_dir
        self.transform = transform
        self.image_files = [f for f in os.listdir(images_dir) if f.endswith(('.png', '.jpg', '.jpeg'))]
        
    def __len__(self):
        return len(self.image_files)
    
    def __getitem__(self, idx):
        img_name = self.image_files[idx]
        image_path = os.path.join(self.images_dir, img_name)
        label_path = os.path.join(self.labels_dir, img_name.replace('.jpg', '.txt').replace('.png', '.txt').replace('.jpeg', '.txt'))
        
        # Load image
        image = Image.open(image_path).convert('RGB')
        
        # Load label
        with open(label_path, 'r') as f:
            label = f.read().strip()  # Read label file, modify as needed
        
        if self.transform:
            image = self.transform(image)
        
        return image, label

# Define the transform
transform = transforms.Compose([transforms.Resize((640, 640)), transforms.ToTensor()])

# Path to validation dataset
dataset_root_path = path_to_root_dir
images_dir = os.path.join(dataset_root_path, 'images')
labels_dir = os.path.join(dataset_root_path, 'labels')

# Instantiate uncompressed model
core = ov.Core()
model = core.read_model(model_path)

# Instantiate the custom dataset
val_dataset = CustomDataset(images_dir=images_dir, labels_dir=labels_dir, transform=transform)
dataset_loader = DataLoader(val_dataset, batch_size=1, shuffle=True)```

Optimize model using NNCF Post-training Quantization API

import nncf  # Neural Network Compression Framework

# Step 1: Initialize transformation function
def transform_fn(data_item):
    """
    Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
    Parameters:
       data_item: Tuple with data item produced by DataLoader during iteration
    Returns:
        input_tensor: Input data for quantization
    """
    img = data_item[0].numpy()
    input_tensor = image_to_tensor(img)
   
    return input_tensor

# Step 2: Initialize NNCF Dataset
calibration_dataset = nncf.Dataset(dataset_loader, transform_fn)
ignored_scope = nncf.IgnoredScope(
    names=[
        "/model.22/cv3.0/cv3.0.0/conv/Conv",  # in the post-processing subgraph
    	"/model.16/conv/Conv",
    	"/model.22/cv2.0/cv2.0.0/conv/Conv",
    	"/model.6/cv1/conv/Conv",
    	"/model.22/cv3.1/cv3.1.1/conv/Conv",
    	"/model.21/cv1/conv/Conv",
    	"/model.21/m.0/cv1/conv/Conv",
    	"/model.7/conv/Conv",
    	"/model.12/cv1/conv/Conv",
    	"/model.4/cv1/conv/Conv",
    	"/model.22/cv2.2/cv2.2.1/conv/Conv",
    	"/model.22/cv2.0/cv2.0.1/conv/Conv",
    	"/model.22/dfl/conv/Conv",
    	"/model.22/cv3.2/cv3.2.2/Conv",
    	"/model.22/cv3.0/cv3.0.2/Conv",
    	"/model.15/cv1/conv/Conv",
    	"/model.5/conv/Conv",
    	"/model.0/conv/Conv"
    ]
)
# Detection model
quantized_model = nncf.quantize(model, calibration_dataset, preset=nncf.QuantizationPreset.MIXED,
ignored_scope=ignored_scope)
quantized_model_save_path = path_to_save_quantized_model
ov.save_model(quantized_model, quantized_model_save_path)

Installed packages

nncf==2.7.0                  
openvino==2023.2.0
ultralytics==8.1.22               

Even, I tried without ignored_scope arguments, but still getting the same results. Please help me out in this context.