Closed AI-P-K closed 2 months ago
It seems your mask is not aligned with the expected ones. Try to produce the agnostic masks by modifying provided script to process your custom datasets.
Hello,
I have tried to use preprocess_agnostic_mask.py as you suggested, but now i am a bit confused.. this script looks for some structure that does not match with the structure i provide below.
I am a little bit confused i thought all data that i need is already there, let me add more details to my use case:
The command i use for inference is: python inference.py --dataset "vitonhd" --data_root_path "test_data/" --output_dir "output/"
The structure of my test_data is as follows:
Can i kindly ask how did you figured that my mask is not aligned with the expected ones?
PS Thank you for your fast reply
Hello, 1 and 2 you were correct... i have used the preprocess_agnostic_mask.py and now it dresses the model better. There is still some quality issues like a distorted face and sometimes, distorted clothing.
Do you suggest to change the local inference to 1024x768?
So yes, i apologize for the stupid question. You were right with all the above, i appreciate your help and fast response. You have a fantastic day!
p.s changing the inference resolution to 1024x768 locally solved the annomalies issues
This depends on what your purpose for local inference is. If it's for generating a large number of high-quality results in parallel, 1024 would be better. If it's for calculating metrics, whether to use 512 or 1024 depends on the baseline you want to compare.
I'm glad your issues have been resolved 😄!
Hello,
I have installed CatVTON locally, downloaded the repository, created a new anaconda environment. I created a custom dataset that has the following:
Original input images: - original person image - original clothing image
Processed data: - agnostic-mask - cloth-mask - image_parse-v3 - openpose_img dude2_keypoints.json - openpose_json
Results: -> Local Result (bad) -> Online Result (good)
What steps am i missing ? is this a models issues? can you provide some extra information about which models we should use?