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I have one confusing question, I can successfully run the codes (both Train and Test on segcapsr3) using both MSCOCO17 and my own grey images. However, I found the final output and raw output images (stored in the folder of ../SegCaps/data/results/segcapsr3/split_0) are always the same, no matter what input images are given?
Any suggestions will be appreciated. The following is my testing code.
The program will convert all image files into numpy format and store training/testing images into ./data/np_files and testing (and training) file lists under ./data/split_list folders. You need to remove these two folders every time if you want to replace your training image and mask files. The program will only read data from np_files folders.
@Cheng-Lin-Li Thank you for your nice work!
I have one confusing question, I can successfully run the codes (both Train and Test on segcapsr3) using both MSCOCO17 and my own grey images. However, I found the final output and raw output images (stored in the folder of ../SegCaps/data/results/segcapsr3/split_0) are always the same, no matter what input images are given?
Any suggestions will be appreciated. The following is my testing code.
python3 ./main.py --test --Kfold 2 --net segcapsr3 --data_root_dir=data --loglevel 2 --which_gpus=-2 --gpus=0 --dataset mscoco17 --weights_path saved_models/segcapsr3/split-0_batch-1_shuff-1_aug-1_loss-dice_slic-1_sub--1_strid-1_lr-0.1_recon-131.072_model_20190918-151252.hdf5
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