update experiments

Author: shjo-april

README.md

@@ -55,13 +55,13 @@ CUDA_VISIBLE_DEVICES=0 python3 train_affinitynet.py --architecture resnest101 --
 ## 3. Train the segmentation model using the pseudo-labels
 3.1. Make segmentation labels to train segmentation model.
 ```bash
-CUDA_VISIBLE_DEVICES=2 python3 inference_rw.py --architecture resnest101 --model_name AffinityNet@ResNeSt-101@Puzzle --cam_dir ResNeSt101@Puzzle@optimal@train@scale=0.5,1.0,1.5,2.0 --domain train_aug --data_dir $your_dir
+CUDA_VISIBLE_DEVICES=0 python3 inference_rw.py --architecture resnest101 --model_name AffinityNet@ResNeSt-101@Puzzle --cam_dir ResNeSt101@Puzzle@optimal@train@scale=0.5,1.0,1.5,2.0 --domain train_aug --data_dir $your_dir
 python3 make_pseudo_labels.py --experiment_name AffinityNet@ResNeSt-101@Puzzle@train@beta=10@exp_times=8@rw --domain train_aug --threshold 0.35 --crf_iteration 1 --data_dir $your_dir
 ```
 
 3.2. Train segmentation model.
 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train_segmentation.py --backbone resnest101 --mode normal --use_gn False --tag DeepLabv3+@ResNeSt-101 --label_name AffinityNet@ResNeSt-101@Puzzle@train@beta=10@exp_times=8@rw@crf=1 --data_dir $your_dir
+CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train_segmentation.py --backbone resnest101 --mode fix --use_gn True --tag DeepLabv3+@ResNeSt-101@Fix@GN --label_name AffinityNet@ResNeSt-101@Puzzle@train@beta=10@exp_times=8@rw@crf=1 --data_dir $your_dir
 ```
 
 ## 4. Evalute the models