GPONet: Two-Stream Gated Progressive Optimization Network for Salient Object Detection

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This is a pytorch implementation of GPONet (Gated Progressive Optimization Network)

requirements

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• torch 2.0.1+cu117
• torchvision 0.15.2+cu117
• matplotlib 3.7.1
• pillow 10.0.0
• tqdm 4.65.0
• numpy 1.24.3

pip install -r requirements.txt

Training and inference

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Training

1. Download the DUTS dataset from Baidu and place it in the data/ folder
2. Download the pre-trained backbone model from Baidu and place it in the model/backbone folder
3. Runing the training process by command python train.py

Inference

1. Download the pre-trained model GPONet_t.pth from Baidu and place it in the save_models/ folder
2. Runing the inference process by command python infer.py

We also provide the predicted saliency maps for DUTS-TE, ECSSD, PASCAL-S, HKU-IS, DUT-OMRON

GPONet Architecture

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Quantitative Comparisons

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Visual Comparisons

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Demo

1. Put the local files in the test_images/ folder and run python train.py
2. Some of the results are as follows: