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Pytorch-Segmentation-multi-models (0, 2021-02-06)
Pytorch-Segmentation-multi-models\.idea (0, 2021-01-20)
Pytorch-Segmentation-multi-models\.idea\inspectionProfiles (0, 2020-12-06)
Pytorch-Segmentation-multi-models\.idea\inspectionProfiles\profiles_settings.xml (174, 2020-12-06)
Pytorch-Segmentation-multi-models\.idea\misc.xml (307, 2020-12-06)
Pytorch-Segmentation-multi-models\.idea\modules.xml (325, 2020-12-06)
Pytorch-Segmentation-multi-models\.idea\Pytorch-Segmentation-multi-models.iml (485, 2020-12-06)
Pytorch-Segmentation-multi-models\.idea\workspace.xml (7764, 2021-01-20)
Pytorch-Segmentation-multi-models\1706.05587.pdf (2855206, 2020-12-11)
Pytorch-Segmentation-multi-models\dataset (0, 2020-12-06)
Pytorch-Segmentation-multi-models\dataset\camvid.py (3330, 2020-04-08)
Pytorch-Segmentation-multi-models\dataset\DRIVE (0, 2018-08-07)
Pytorch-Segmentation-multi-models\dataset\drive.png (433119, 2020-04-08)
Pytorch-Segmentation-multi-models\dataset\DRIVE\test (0, 2018-08-07)
Pytorch-Segmentation-multi-models\dataset\DRIVE\test\1st_manual (0, 2018-08-07)
Pytorch-Segmentation-multi-models\dataset\DRIVE\test\1st_manual\01_manual1.gif (11548, 2004-01-20)
Pytorch-Segmentation-multi-models\dataset\DRIVE\test\1st_manual\02_manual1.gif (12065, 2004-01-20)
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Pytorch-Segmentation-multi-models\dataset\DRIVE\test\1st_manual\20_manual1.gif (9701, 2004-01-20)
Pytorch-Segmentation-multi-models\dataset\DRIVE\test\2nd_manual (0, 2018-08-07)
Pytorch-Segmentation-multi-models\dataset\DRIVE\test\2nd_manual\01_manual2.gif (11813, 2004-01-21)
Pytorch-Segmentation-multi-models\dataset\DRIVE\test\2nd_manual\02_manual2.gif (11859, 2004-01-21)
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# Pytorch-Segmentation-multi-models Pytorch implementation for Semantic Segmentation with multi models for blood vessel segmentation in fundus images of DRIVE dataset. Deeplabv3, Deeplabv3_plus, PSPNet, UNet, UNet_AutoEncoder, UNet_nested, R2AttUNet, AttentionUNet, RecurrentUNet, SEGNet, CENet, DsenseASPP, RefineNet, RDFNet # Data Available at https://www.isi.uu.nl/Research/Databases/DRIVE/ # Training python train.py --model unet You can modify --model to change models. # Reference: AttentionR2Unet: Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation https://arxiv.org/abs/1802.06955 AttentionUnet: Attention U-Net: Learning Where to Look for the Pancreas https://arxiv.org/abs/1804.03999 CENet: CE-Net: Context encoder network for 2D medical image segmentation https://arxiv.org/abs/1903.02740 DeepLabV3: Rethinking Atrous Convolution for Semantic Image Segmentation https://arxiv.org/pdf/1706.05587.pdf DeepLabV3_plus: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation https://arxiv.org/pdf/1802.02611.pdf DenseASPP: DenseASPP for Semantic Segmentation in Street Scenes http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf PSPNet: Pyramid Scene Parsing Network https://arxiv.org/abs/1612.01105 RDFNet: RDFNet: RGB-D Multi-level Residual Feature Fusion for Indoor Semantic Segmentation RecurrentUnet: Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation https://arxiv.org/ftp/arxiv/papers/1802/1802.06955.pdf RefineNet: RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation https://arxiv.org/pdf/1611.06612.pdf SegNet: SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation https://arxiv.org/abs/1511.00561 U-Net: Convolutional Networks for Biomedical Image Segmentation https://arxiv.org/abs/1505.04597 Unet_nested: Unet++: A Nested U-Net Architecture for Medical Image Segmentation https://arxiv.org/pdf/1807.10165.pdf Github: https://github.com/Guzaiwang/CE-N https://github.com/ShawnBIT/UNet-family https://github.com/charlesCXK/PyTorch_Semantic_Segmentation

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