You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
{{ message }}
This repository has been archived by the owner on Sep 2, 2024. It is now read-only.
On Fri, May 31, 2024 at 1:05 PM Chris ***@***.***> wrote:
Hi author, thank you very much for the fantastic work!
May I ask, to train your model, do I need to prepare labeled data from two
different datasets?
Assuming that one of the datasets has no annotation data, is the method
feasible? Or even both datasets have no labels?
Looking forward to your feedback!
Best,
Chris
—
Reply to this email directly, view it on GitHub
<#67>, or
unsubscribe
<https://github.com/notifications/unsubscribe-auth/AS6J47KV7M45FBYPGF5VJO3ZFCUVLAVCNFSM6AAAAABITE5UM6VHI2DSMVQWIX3LMV43ASLTON2WKOZSGMZDQMRVGM4TCNY>
.
You are receiving this because you are subscribed to this thread.Message
ID: ***@***.***>
Thank you, so may I ask, if I want to prepare two datasets now, A and B, for A, I need to provide RGB and G-buffer, and for B, I only need to provide RGB, is this feasible? Very much looking forward to your answer.
Hi author, thank you very much for the fantastic work!
May I ask, to train your model, do I need to prepare labeled data from two different datasets?
Assuming that one of the datasets has no annotation data, is the method feasible? Or even both datasets have no labels?
Looking forward to your feedback!
Best,
Chris
The text was updated successfully, but these errors were encountered: