Hi there! Amazing repo you have here. I already tried both training and inferencing but i have few question to ask.
Do you have the image prediction output for this repository that you already tried before?
Cuz i already tried both training and inferencing using this repo, but the result was really bad.
I trained with 356 Image-Mask data.
For _dataset.py, why there are a lot of complicated process like ImageTargetDataset, RandomConncatDataset etc.?Why can't just use normal image loader practice like in keras or maybe just load images-mask using opencv and append them on list instead of appending their path?
in _visdataset(), why i can only visualize the Image data and not the Target(Mask)? I tried but errors occurred. How can i know the images have correct corresponding mask?
in Testing Image section at train-mobilenet.ipynb, what exactly would be the output? When i tried, it don't even display/show the prediction mask. It outputs back the original test image. When i try to output "out_img" variable, noisy images were displayed.
Hi there! Amazing repo you have here. I already tried both training and inferencing but i have few question to ask.
Do you have the image prediction output for this repository that you already tried before? Cuz i already tried both training and inferencing using this repo, but the result was really bad. I trained with 356 Image-Mask data.
For _dataset.py, why there are a lot of complicated process like ImageTargetDataset, RandomConncatDataset etc.? Why can't just use normal image loader practice like in keras or maybe just load images-mask using opencv and append them on list instead of appending their path?
in _visdataset(), why i can only visualize the Image data and not the Target(Mask)? I tried but errors occurred. How can i know the images have correct corresponding mask?
in Testing Image section at train-mobilenet.ipynb, what exactly would be the output? When i tried, it don't even display/show the prediction mask. It outputs back the original test image. When i try to output "out_img" variable, noisy images were displayed.