I have made custom data set and labels with Matlab code and the below python scripts:

def gen_csv(dir_list,
            csv_path,
            ext=".bmp",
            data_mode="syn",
            write_mode="w"):
    """
    Generate CSV file from dir list
    :param data_mode: syn | ref | ugc
    :param write_mode: w | a+
    """
    if len(dir_list) == 0:
        print("[Err]: empty dir list.")
        return

    print("Data mode: ", data_mode)
    print("Write mode: ", write_mode)

    N_DISTORTIONS = 25
    N_CLS = N_DISTORTIONS * 5 + 1
    print("N_CLS: {:d}".format(N_CLS))
    cnt = 0
    with open(csv_path, write_mode, encoding="utf-8") as f:
        if write_mode != "a+":
            f.write(",Unnamed: 0,File_names,labels\n")
        for dir_path in dir_list:
            print("Processing {:s}...".format(dir_path))
            img_names = [x for x in os.listdir(dir_path) if x.endswith(ext)]
            for img_name in img_names:
                img_path = dir_path + "/" + img_name
                if not os.path.isfile(img_path):
                    continue

                print("processing {:s}".format(img_path))

                items = img_name.split(".")[0].split("_")
                # print(items)

                if len(items) > 3 and data_mode == "syn":
                    distort_type = int(items[-2])
                    distort_level = int(items[-1])

                f.write('{:d},{:d},{:s},"['.format(cnt, cnt, img_path))

                for i in range(N_CLS):
                    if data_mode == "syn":
                        if i == (distort_type - 1) * 5 + (distort_level - 1) + 1:
                            if i == N_CLS - 1:
                                f.write("1.0")
                            else:
                                f.write("1.0, ")
                        elif 0 <= i < N_CLS - 1:
                            f.write("0.0, ")
                        elif i == N_CLS - 1:
                            f.write("0.0")
                    elif data_mode == "ref":
                        if i == 0:
                            f.write("1.0, ")
                        elif 0 <= i < N_CLS - 1:
                            f.write("0.0, ")
                        elif i == N_CLS - 1:
                            f.write("0.0")
                    elif data_mode == "ugc":
                        if 0 <= i < N_CLS - 1:
                            f.write("0.0, ")
                        elif i == N_CLS - 1:
                            f.write("0.0")
                    else:
                        print("[Err]: invalid data mode, data mode should be:"
                              "syn | ref | ugc")
                f.write(']"\n')

                cnt += 1

the loss reduced from 11.7 to 3.4 now after 29 epoches...

How to train the seond stage regressor?

Thank you for your excellent work and contribution! I wonder how to train CONTRIQUE on my own datasets, not considering the linear projector training.

Is the distributed training set up by you single-machine multi-card or multi-machine multi-card? I ran into NCCL errors training on two Gpus on the same machine

In case of custom dataset training requirements, and to avoid downloading KaDID-30K dataset, it's recommended to provide the link of synthesizing image distortions Matlab scripts. Because the dataset is so large(40+G)...

Using the provided datasets i am facing an issue regarding the regressor training. I have trained the model but i don't understand how the scores.npy file is generated

http://database.mmsp-kn.de/kadid-10k-database.html links are not working and alternative link ref_img folder is empty! this data set is very important in your work, i don't know how to train the network without it.

When training, I find training with pretrained resnet50 model takes less trainning time.I want to know why the article choose to training from the start without pretrained?

Hi, thanks for this work. I want to use this metric to evaluate the quality of some image for my research, in both FR and NR mode. I was wondering what is the score range for FR and the score range for NR. How should we interpret this values? For examples, the NR score for churchandcapitol.bmp is 16.765732, but on image66.bmp which is distorted, the NR score is 56.64854 while using the same linear regressor LIVE for both. On the other hand, with CLIVE I obtain NR score 84.52191 for churchandcapitol.bmp and 23.473944 for image66.bmp.

Higher is better? Lower is better? I've noticed that using different linear regressor the output values are in different ranges.

I also tried to compute the FR between two identical images (churchandcapitol.bmp) and the results is -0.04409191 with CSIQ_FR linear regressor, and 1.8522606 with linear regressor LIVE_FR. Also, when I compare image 33.bmp with itself I obtain the same value of -0.04409191 with CSIQ_FR and 1.8522606 with LIVE_FR.

Could you please provide some insights on the interpretation of the scores?

Thank you for your time. Have a great day.

Hi Found the paper very useful and trying to Finetune on my own dataset. I wanted to know what the loss values looked like for your dataset. According to the experiments I am running, the loss doesn't converge and is stuck around 3.9-4 even after extensive training.

• my dataset contains around 2.5 L image (100k synthetic + 150k UGC).
• Using a learning rate of 0.001 for later epochs. Any Ideas on how to improve the training regimen so that the model converges?

@pavancm Hi all,

I am looking to train a model using custom dataset where I have only image. Can you please help me out with any explanation, idea for training process. Please tell me step by step for training with my data

Thanks!!

In case of custom dataset training requirements, and to avoid download KaDID-30K dataset, it's recommended to provide the link of synthesizing iamge distortions Matlab scripts. Because the dataset is so large(40+G)...

I have tried training for different color space,and get similar result in RGB、Graysacle.But I failed to get a good result in MS-colorspace.Is there are something special I should take into consider when training through MS-colorspace?And is large amount of data for pretraining essential?

Hello，I wonder the performance of supervised finetune using CONTRIQUE encoder compared to imagenet pretrained model, but I can't find such exp in paper.

can you share the results if you have done such exp, Thanks.