IMPORTANT: This is a modified version of this repository. I modified the code for my thesis. Read the instructions of the original authors first.
I used this code for my thesis. IMPORTANT: If you use my script to download the VGG16 model from pytorch check the default value of the pytorch environment variable TORCH_MODEL_ZOO: I used this code on Ubuntu 18.04 LTS and ~/.torch/models was the default value; if the value is different, modify the last instruction of the script file.

• For my thesis I trained the CPD model with these options

  • NVIDIA GeForce 750 Ti 2 GB VRAM
  • training dataset = MSRA-5K
  • backbone model = VGG16
  • batch size = 2
  • learning rate = 0.0001
  • epoch = 10
  • no validation set

  I ended up with an average loss of 0.2954(more info here).

• I added an unique naming of trained models to keep track of my runs.

• I used wandb wrapper code to monitor the training.

• I added more printed info while training and testing.

• I tested my trained model on the datasets PASCAL-S, ECSSD, HKU-IS, DUTS Test Set and DUT-OMRON(you can see the saliency maps in the 'results' folder).

• You can see quantitative results in this images(there are other models results too as I evaluated and compared multiple models).