I used torchvision.model.resnet18 to classify images, but it seems output same result with different input.
Here is my test code
res_model = torchvision.models.resnet18(pretrained=True)
normalize = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )
])
res_model.to(device)
def predict_image(image_path, model):
    image = Image.open(image_path)
    image_tensor = normalize(image).float()
    image_tensor = image_tensor.unsqueeze_(0)
    image_tensor = image_tensor.to(device)
    output = model(image_tensor)
    output_c = output.cpu()
    index = output_c.data.numpy().argmax()   
    return index


Here are some test case with different images and the same output


Though I don’t know what the index means, I think the same index means the same category.
Did I do something wrong?
Thanks in advance.