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.