rshaojimmy / rfmetafas Goto Github PK

I want to compare the ROC curves of some methods for domain generalization.
Could you please provide optimal training model? Thank you.
My Email address:[email protected]

I have some problem about how to process the origin dataset.

1. How do you change the video data into image? How often should I get a screenshot? (Such as every 5 frames?) Could you tell me the whole num of images in every dataset after the pre-processing?
2. Should the input image been croped to face part or remain the whole scene?(In my experiment, PRNet can only generate depth for face image.)

Thanks for your great work and hope for relpy!

Is the depth map label range 0-255 or 0-1? If it is the latter, what is the normalization method?

这是数据集自带的吗

Hi,
thanks for sharing the code.
I have some questions about your project that I would like to confirm.
1.What did your meta learner mainly learn?(FeatExtractor Weight or some hyperparameters?)
2.When testing, is it not necessary to generate a face depth map? because it is only used to auxiliary supervision, like an image label?
3.we need to generate a depth map through PRNet and add it to the input ? or your code contains this generate function?
4.Is it possible to achieve real-time detection?

Thanks.

Wait for the pre-trained model?

您好，我按照您的要求送入网络rgb人脸图片，但是不管是real还是fake的样本，分数都在0.5左右，没有大的变化。请问您遇到过这种问题吗？ @ @rshaojimmy

Hi,

Thanks for sharing your code.
I have installed and setup the prerequisite software.
However when I run the Testing mode using this command "python main.py --training_type Test", I encountered the following error. The program main.py seems to be asking for the command line argument parameter tstfile.
Appreciate your advice. Thanks

File "main.py", line 175, in
main(parser.parse_args())
File "main.py", line 22, in main
savefilename = osp.join(args.tstfile, args.tstdataset+'to'+args.dataset_target+args.snapshotnum)
AttributeError: 'Namespace' object has no attribute 'tstfile'

Best Regards,
Benjamin

Hello, I'm Minha Kim researching anti-spoofing in korea.
Firstly, I appreciate you sharing this method with the public.

I have one question, how did you get ~_depth images?
As I know, all datasets don't contain depth images.
So, I wonder how did you make or get the dataset as a depth type of image.

Thank you :)
With best regards,
Minha Kim.

您可以提供一下fake_depth.jpg吗，所有假脸的深度是不是都假设为0了，那么您在训练的过程中用的fake_depth.jpg是什么样的呢，可以描述一下吗

nice, it is a good job.but,when i run it ,some quetions occers.as shown below:

i do not know the composition of documents,please show me the hierarchy of data set files. looking forward to your replay.

I am trying to run the Test method but keep running into errors, can you please mention what are all the required files and settings that I would need to be able to run it.

if getreal and name=='idiap':
depth_dir = os.path.join('depth', dirlist[0], dirlist[1], dirlist[2], imgname + '_depth.jpg')
elif getreal and name=='CASIA':
depth_dir = os.path.join('depth', dirlist[0], dirlist[1], dirlist[2], imgname + '_depth.jpg')
elif getreal and name=='MSU':
depth_dir = os.path.join('depth', dirlist[0], dirlist[1], imgname + '_depth.jpg')
elif getreal and name=='OULU':
depth_dir = os.path.join('depth', dirlist[0], imgname + '_depth.jpg')
else:
==> depth_dir = os.path.join('depth', 'fake_depth.jpg')

Why you let all name of fake_depth_image are 'fake_depth.jpg' ?
Did you not use them?

It's a good job, but it doesn't mention aim-fas. Aim-fas is similar to this article.

https://github.com/qyxqyx/AIM_FAS

I have some questions about the code In datasets/DatasetLoader.py :

if getreal:
            filename = 'image_list_real.txt'
        else:
            filename = 'image_list_fake.txt'

What's the content in image_list_real.txt about and how do you get these files?
I cannot get these files after I download the datasets so I am curious about it.

I don't know the content of the txts, such as image_list_all.txt. Hope you can share them, thanks a lot

Hi, a great job. But when I reuse your code to trian model by myself, I encounter the following problems:

1. I find the model is too large, and when training, the results are very shocking. For example, when I use the epoch100 trained_model to test, the ACC in testing-set is 80%, but when I use the epoch101 trained_model to test, the ACC in testing-set is below 60%. Do you know why?

2. I cannot reproduce the results of the C&I&M to O scenario in your paper. The best AUC results that I achieved by using your code is 82%, which has a large gap compared to the results published in your paper 91.16%. How do you test your model on Oulu dataset? Do you use the all data (training and testing) or just the testing data? Why I cannot reproduce the results?

Thank you very much!!

I am sorry for the inconveninent I have caused. I would like to ask, do you reproduce the code of MetaReg？

I am confused about the path processing of the data txt file. Can you share the specific path format of the txt file? Thank you!

i see the hsv image also as the input in your code, and don't mark it in your paper . can you tell me what information dose hsv image can provide?

Nice job! I do not have enough data, but I want to test the performance of this model.