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only ~50% tampering mask detected #8
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Could you share your testing steps and what is your testing data? |
I followed exactly same procedure as mentioned
Also, the images for which correct tampering mask predicted are different when I tried running the script again. |
Thanks for your reminder! Please set the ''degrade_shuffle=False'' in Line 25 of the "options/test_editguard.yml" since the uploaded checkpoint is trained under the clean condition. We have updated the file in our code~ |
Thank you for checking. It is working quite well after that. Are you planning to release the model trained with degraded images as well? |
@Amit507017 , it's so good that you got the project running. How did you achieved this? Any hint on how you got it running, thanks |
@Amit507017 Hello, it's so good that you got the project running. I tried to reproduce this experiment, but I have some dependencies that cannot be downloaded, can you tell me what python environment you are configuring the environment in? |
@super-danny Hello, has your problem been solved? I can't get the project to run, I can't install some of the dependencies in it, can you install the dependencies normally? |
Sorry for late reply. I faced some problems with dependencies as well. Exactly I do not remember but I just relaxed some dependencies. |
May I ask if you have finished debugging this project? May I ask if the code is complete? My second step python app.py doesn't work, and what's in Train2017.txt? Just the file name? |
After downloading COCO2017 and extracting all files, use |
Hello,
very nice work and thank you for sharing!
I followed the procedure for testing the approach exactly as mentioned in Readme. In the test, roughly in 50% of images only the tampered mask is predicted. In the remaining images complete white image is detected. Also when I ran the test multiple times, I see different set of images are correctly predicted. Any hints why this might be happening? Is someone else also experiencing this behaviour?
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