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Evgenii Pishchik 2022-04-09 02:49:57 +03:00
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4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb
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@ -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",
"<img src=\"images/segm.png\" width=\"50%\">\n",
"<img src=\"images/segm.png\" width=\"80%\">\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",
"<img src=\"images/segnet.png\" width=\"50%\">\n",
"<img src=\"images/segnet.png\" width=\"80%\">\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",
"<img src=\"images/unet.png\" width=\"50%\">\n",
"<img src=\"images/unet.png\" width=\"70%\">\n",
"\n",
"* Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)"
]