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README.md

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+# TRACER: Extreme Attention Guided Salient Object Tracing Network implementation in PyTorch
+
+This paper was accepted at AAAI 2022 SA poster session.  
+
+## Datasets
+All datasets are available in public.
+* Download the DUTS-TR and DUTS-TE from [Here](http://saliencydetection.net/duts/#org3aad434)
+* Download the DUT-OMRON from [Here](http://saliencydetection.net/dut-omron/#org96c3bab)
+* Download the HKU-IS from [Here](https://sites.google.com/site/ligb86/hkuis)
+* Download the ECSSD from [Here](https://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/dataset.html)
+* Download the PASCAL-S from [Here](http://cbs.ic.gatech.edu/salobj/)
+* Download the edge GT from [Here]().
+
+## Data structure
+<pre><code>
+TRACER
+├── data
+│   ├── DUTS
+│   │   ├── Train
+│   │   │   ├── images
+│   │   │   ├── masks
+│   │   │   ├── edges
+│   │   ├── Test
+│   │   │   ├── images
+│   │   │   ├── masks
+│   ├── DUT-O
+│   │   ├── Test
+│   │   │   ├── images
+│   │   │   ├── masks
+│   ├── HKU-IS
+│   │   ├── Test
+│   │   │   ├── images
+│   │   │   ├── masks
+      .
+      .
+      .
+</code></pre>
+
+## Requirements
+* Python 3.7.x
+* Pytorch >= 1.8.0
+* albumentations >= 0.5.1
+* matplotlib >= 3.3.3
+* tqdm >=4.54.0
+* scikit-learn > 0.23.2
+
+## Run
+* Run **main.py** scripts.
+<pre><code>
+# For training TRACER-TE0 (e.g.)
+python main.py train --arch b0 --img_size 320
+
+# For testing TRACER with pre-trained model (e.g.)  
+python main.py test --exp_num 0 --arch b0 --img_size 320
+</code></pre>
+
+## Configurations
+--img_size: Input image resolution.  
+--arch: EfficientNet backbone scale: TE0 to TE7.  
+--frequency_radius: High-pass filter radius in the MEAM.  
+--gamma: channel confidence ratio \gamma in the UAM.   
+--denoise: Denoising ratio d in the OAM.  
+--RFB_aggregated_channel: # of channels in receptive field blocks.  
+--multi_gpu: Multi-GPU learning options.  
+
+
+
+
+