Comments (6)
That is the error about validation parts. Check the validation and there is not validation data. in that cases np.random.randint(0,0) has syntax error.
from ext_portrait_segmentation.
thanks
and could you tell me what the Portraint dir contain?
just train.txt?
from ext_portrait_segmentation.
I do not know also. I am not ans author. What I assume is that, Portrait folder is original folder. aug file is extra. What I have done was remove validation parts and change some parts in dataloader.py and main parts. However the reason what I try to use this code is apply my own network. So I use my own train in the end. Most of the issue will happen the log parts. Just remove the log and checkpoint part. It will work ok. Thank you.
from ext_portrait_segmentation.
Hello,
First, the line 25 rand_pick is not necessary for training.
This randomly picks one of result from validation set for visualization with visdom
from ext_portrait_segmentation.
please refer one of my answer
from ext_portrait_segmentation.
Hello,
First, the line 25 rand_pick is not necessary for training.
This randomly picks one of result from validation set for visualization with visdom
what if data is imbalanced
from ext_portrait_segmentation.
Related Issues (20)
- the size of input_image HOT 1
- Accuracy issue, implementation difference compared to paper. HOT 4
- visualizing results HOT 4
- The speed about ExtremeC3Net. HOT 1
- Rough edges in mask? HOT 2
- Training new dataset
- main.py HOT 1
- Testing new Image HOT 2
- How to use EG1800
- Error in Visualize_video.py when using result/SINet/SINet.pth HOT 5
- Why is the label of eg1800 data set inconsistent with the number of images? HOT 3
- about test!!! HOT 4
- Converting .pth model to onnx HOT 7
- about inference time
- why ignore_idx==255? in lovasz loss? HOT 2
- Pretrained model output bad segmentation result HOT 4
- trainning new data and test error, help please HOT 3
- Model does not perform well on real world portrait images/videos HOT 1
- groups config setting
- custom dataset training HOT 1
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