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.