ADNet Implementation using Tensorflow

Requirements

1.python
2.tensorflow
3.numpy PIL

Test

python main.py

--use_gpu=1 \                           # use gpu or not  
--gpu_idx=0 \  
--gpu_mem=0.5 \                         # gpu memory usage  
--phase=test \  
--test_dir=/path/to/your/test/dir/ \  
--save_dir=/path/to/save/results/ \  

Train
put your dataset in ./data
python main.py

--use_gpu=1 \                           # use gpu or not  
--gpu_idx=0 \  
--gpu_mem=0.5 \                         # gpu memory usage 
--phase=train \  
--epoch=100 \                           # number of training epoches  
--batch_size=16 \  
--patch_size=48 \                       # size of training patches  
--start_lr=0.001 \                      # initial learning rate for adm  
--eval_every_epoch=20 \                 # evaluate and save checkpoints for every # epoches  
--checkpoint_dir=./checkpoint           # if it is not existed, automatically make dirs  
--sample_dir=./sample                   # dir for saving evaluation results during training

You can read more details in https://blog.csdn.net/sf_qw39/article/details/105161957
If you find any problem when running the code, please contact to me.