But sorry for asking silly questions:
In the paper, you show the average testing speed for an image with DPT-Large is around 35ms, which is very promising!
But when using your online DPT-Large App to test the depth prediction performance, it usually takes even longer than 30 seconds to estimate one image's depth info. Is it just because of the bad internet connection?
Meanwhile, it seems your trained model for the DPT-Large depth prediction network has a relatively big size (around 1.3G), which specific technique help accelerate the inference?
Sorry for so many questions and look forward to your reply!
Thank you so much for your great work!!!
But sorry for asking silly questions: In the paper, you show the average testing speed for an image with DPT-Large is around 35ms, which is very promising!
But when using your online DPT-Large App to test the depth prediction performance, it usually takes even longer than 30 seconds to estimate one image's depth info. Is it just because of the bad internet connection?
Meanwhile, it seems your trained model for the DPT-Large depth prediction network has a relatively big size (around 1.3G), which specific technique help accelerate the inference?
Sorry for so many questions and look forward to your reply!