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Scaled-MA-Unet

An improved Multiattention-Unet is divided into four versions of different scales. The original version can refer to code. In addition, we also provide API for other models from segmentation-models-pytorch, such as Unet, UnetPlusPlus, MAnet, Linknet, FPN, PSPNet, DeepLabV3, DeepLabV3Plus.

Quick Start Examples

Install

pip install -r requirements.txt 

• It is worth noting that python 3.7 and torch 1.8 are recommended.

Preparation of datasets

1. All data should be placed in directory “VOCdevkit/VOC2007/”. <br>
   • The name of original image and its corresponding label must be consistent, their format can be different(important) <br> Image: cat_00001.jpg ; Label: cat_00001.png
2. Put all the original images in folder “JPEGImages” and all the labels in folder “SegmentationClass”.<br>
3. Run "voc2unet" to divides training sets and test sets. <br>
   • If your label is not in png format, modify the code in line 17, as follow: <br> if seg.endswith(".png"):
   • You can also modify the 12 lines of code to divide the training set and test set according to other proportions, as follow. The default ratio is 8:2. <br> train_percent = 0.8
   • Finally, four text files will be generated in the "VOCdevkit/VOC2007/ImageSets/" directory.

Training

1. Training with Multi-GPU. （recommended） <br>

   set distributed = True
   python -m torch.distributed.launch --nproc_per_node=num_gpu train.py
   

   If the memory is not released after training, use pgrep python | xargs kill -s 9 <br>

2. Training with single GPU. <br>

   python train.py
   

   • It is worth noting that in the hyperparameters, num_classes should be set to the number of categories plus 1. <br> For example, if you want to segmentation cat and dog in the images, although there are only two categories, <br> you need to set it to 3, because the label of the background is 0.

Prediction and Validation

See predict.py for details

Details of Multi-Attention UNet

You can learn the details of Multi-Attention UNet through the paper as follow, and please cite our papers if the code is useful for your papers. Thank you! <br>

• MDPI and ACS Style <br> Sun, Y.; Bi, F.; Gao, Y.; Chen, L.; Feng, S. A Multi-Attention UNet for Semantic Segmentation in Remote Sensing Images. Symmetry 2022, 14, 906. https://doi.org/10.3390/sym14050906 <br>

• AMA Style <br> Sun Y, Bi F, Gao Y, Chen L, Feng S. A Multi-Attention UNet for Semantic Segmentation in Remote Sensing Images. Symmetry. 2022; 14(5):906. https://doi.org/10.3390/sym14050906 <br>

• Chicago/Turabian Style <br> Yu Sun, Fukun Bi, Yangte Gao, Liang Chen, and Suting Feng. 2022. "A Multi-Attention UNet for Semantic Segmentation in Remote Sensing Images" Symmetry 14, no. 5: 906. https://doi.org/10.3390/sym14050906 <br>

Reference

https://github.com/bubbliiiing/unet-pytorch <br> https://github.com/yassouali/pytorch-segmentation <br> https://github.com/qubvel/segmentation_models.pytorch <br>