ISNet

dependencies

>= Ubuntu 16.04 
>= Python 3.7
>= Pytorch 1.3.0
OpenCV-Python

preparation

• download the official pretrained model ([Baidu drive](https://pan.baidu.com/s/1zRhAaGlunIZEOopNSxZNxw code：fv6m)) of ResNet-50 implemented in Pytorch if you want to train the network again.
• download or put the RGB saliency benchmark datasets ([Baidu drive](https://pan.baidu.com/s/1kUPZGSe1CN4AOVmB3R3Qxg code：sfx6)) in the folder of data for training or test.

generate the boundary envelope

After preparing the data folder, you need to use the enlarge_b.py to produce the boundary envelope, which can be used to generate the Expanded Ground Truth for training. Run this command

python data4/enlarged_b.py

generate the dilated and eroded mask for hierarchical difference-aware loss function

After preparing the data folder, you need to u the dilate_erode.py to generate the dilated and eroded mask for hierarchical difference-aware loss function for training. Run this command

python data4/dilate_erode.py

training

you may revise the TAG and SAVEPATH defined in the train.py. After the preparation, run this command

'CUDA_VISIBLE_DEVICES=0,1,…… python -m torch.distributed.launch --nproc_per_node=x train.py -b 16'

make sure that the GPU memory is enough (You can adjust the batch according to your GPU memory).

test

After the preparation, run this commond to generate the final saliency maps.

 python test.py 

We provide the trained model file ([Baidu drive](link：https://pan.baidu.com/s/1rPpyKnNQPbyA1RVbz1ipjg?pwd=ys6n code：ys6n), and run this command to check its completeness:

cksum ISNet 

you will obtain the result ISNet.

evaluation

We provide the predicted saliency maps on five benchmark datasets,including PASCAL-S, ECSSD, HKU-S, DUT-OMRON and DUTS-TE. ([Baidu drive](link：https://pan.baidu.com/s/1usIegmAAbt9FJAx_FIMoFg?pwd=dkkt code: dkkt)

You can use the evaluation code in the folder "eval_code" for fair comparisons, but you may need to revise the algorithms , data_root, and maps_root defined in the main.m.