From b1302fdf254dd6d2526bd5a41aca9dc28733f4ca Mon Sep 17 00:00:00 2001 From: Evgenii Pishchik <57502208+Pe4enIks@users.noreply.github.com> Date: Sat, 9 Apr 2022 02:49:57 +0300 Subject: [PATCH] image sizes changed --- .../12-Segmentation/SemanticSegmentationPytorch.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb b/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb index cc859e9e..2968bd9f 100644 --- a/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb +++ b/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb @@ -18,7 +18,7 @@ "\n", "Almost all architectures have same structure. First part is **encoder** that extracts features from input image, second part is **decoder** that transforms this features into image with same height and width and some number of channels, may be equal to classes count.\n", "\n", - "\n", + "\n", "\n", "> Image from [this publication](https://arxiv.org/pdf/2001.05566.pdf)" ] @@ -286,7 +286,7 @@ "\n", "Simple encoder - decoder architecture with convolutions, poolings in encoder and convolutions, upsamplings in decoder.\n", "\n", - "\n", + "\n", "\n", "* Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)" @@ -552,7 +552,7 @@ "\n", "U-Net usually has a default encoder for feature extraction, for example resnet50.\n", "\n", - "\n", + "\n", "\n", "* Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)" ]