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NoiseLF
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NoiseLF
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## Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection ### This is a PyTorch implementation of our paper ## Overall  ## Prerequisites - Python 3.6.12 - Pytorch 1.2.0+ - torchvision 0.4.0+ ## Update 1. We released our code for joint training with depth and appearance, which is also our best performance model. ## Usage ### 1. Clone the repository ```shell git clone https://github.com/OLobbCode/NoiseLF.git cd NoiseLF-code/ ``` ### 2. Download the datasets Download the following datasets and unzip them. * [DUT-LF](https://pan.baidu.com/share/init?surl=hq135pTjbwuda0VMocOsxw) dataset，fetch code is ‘vecy’. * [HFUT](https://github.com/pencilzhang/HFUT-Lytro-dataset) dataset. * [LFSD](https://www.eecis.udel.edu/~nianyi/LFSD.htm) dataset. * The .txt file link for testing and training is [here](https://pan.baidu.com/s/1uoVtqM8V19fT6rvqgW__cg), code is 'joaa'. ### 3. Train 1. Set the `c.DATA.TRAIN.ROOT` and `c.DATA.TRAIN.LIST` path in `config.py` correctly. 2. We demo using VGG-19 as network backbone and train with a initial lr of 1e-5 for 30 epoches. 3. After training the result model will be stored under `snapshot/exp_noiself` folder. Note：only support `c.SOLVER.BATCH_SIZE=1` ### 4. Test For single dataset testing: you should set `c.PHASE='test'` in config.py, and set `c.DATA.TEST.ROOT` , `c.DATA.TEST.LIST` as yours. ```shell python demo.py ``` For evaluate : ```shell python evaluate.py ``` All results saliency maps will be stored under `'Test/Out/exp_noiself_30/'` folders in .png formats. Thanks to [MOLF](https://github.com/jiwei0921/MoLF).
