From 548553153122a6f6bcc37a0cbfea0a2706be5f52 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dar=C3=ADo=20Here=C3=B1=C3=BA?= Date: Wed, 1 May 2019 11:01:04 -0300 Subject: [PATCH] Minor syntax issues fixed --- docs/README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/README.md b/docs/README.md index 60f2978c3..6576a2ea8 100644 --- a/docs/README.md +++ b/docs/README.md @@ -6,7 +6,7 @@ ### What's New -- This repo has been depricated and will no longer be handling issues. Feel free to use as is :) +- This repo has been deprecated and will no longer be handling issues. Feel free to use as is :) ## Description This repository serves as a Semantic Segmentation Suite. The goal is to easily be able to implement, train, and test new Semantic Segmentation models! Complete with the following: @@ -53,7 +53,7 @@ The following segmentation models are currently made available: - [Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes](https://arxiv.org/abs/1611.08323). Combines multi-scale context with pixel-level accuracy by using two processing streams within the network. The residual stream carries information at the full image resolution, enabling precise adherence to segment boundaries. The pooling stream undergoes a sequence of pooling operations to obtain robust features for recognition. The two streams are coupled at the full image resolution using residuals. In the code, this is the FRRN model. -- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network](https://arxiv.org/abs/1703.02719). Proposes a Global Convolutional Network to address both the classification and localization issues for the semantic segmentation. Uses large separable kernals to expand the receptive field, plus a boundary refinement block to further improve localization performance near boundaries. +- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network](https://arxiv.org/abs/1703.02719). Proposes a Global Convolutional Network to address both the classification and localization issues for the semantic segmentation. Uses large separable kernels to expand the receptive field, plus a boundary refinement block to further improve localization performance near boundaries. - [AdapNet: Adaptive Semantic Segmentation in Adverse Environmental Conditions](http://ais.informatik.uni-freiburg.de/publications/papers/valada17icra.pdf) Modifies the ResNet50 architecture by performing the lower resolution processing using a multi-scale strategy with atrous convolutions. This is a slightly modified version using bilinear upscaling instead of transposed convolutions as I found it gave better results. @@ -63,7 +63,7 @@ to obtain robust features for recognition. The two streams are coupled at the fu - [DenseASPP for Semantic Segmentation in Street Scenes](http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.html). Combines many different scales using dilated convolution but with dense connections -- [Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation](http://openaccess.thecvf.com/content_cvpr_2018/html/Bilinski_Dense_Decoder_Shortcut_CVPR_2018_paper.html). Dense Decoder Shorcut Connections using dense connectivity in the decoder stage of the segmentation model. **Note: this network takes a bit of extra time to load due to the construction of the ResNeXt blocks** +- [Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation](http://openaccess.thecvf.com/content_cvpr_2018/html/Bilinski_Dense_Decoder_Shortcut_CVPR_2018_paper.html). Dense Decoder Shortcut Connections using dense connectivity in the decoder stage of the segmentation model. **Note: this network takes a bit of extra time to load due to the construction of the ResNeXt blocks** - [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://arxiv.org/abs/1808.00897). BiSeNet use a Spatial Path with a small stride to preserve the spatial information and generate high-resolution features while having a parallel Context Path with a fast downsampling strategy to obtain sufficient receptive field. @@ -85,7 +85,7 @@ to obtain robust features for recognition. The two streams are coupled at the fu - **models:** Folder containing all model files. Use this to build your models, or use a pre-built one -- **CamVid:** The CamVid datatset for Semantic Segmentation as a test bed. This is the 32 class version +- **CamVid:** The CamVid dataset for Semantic Segmentation as a test bed. This is the 32 class version - **checkpoints:** Checkpoint files for each epoch during training @@ -112,7 +112,7 @@ The only thing you have to do to get started is set up the folders in the follow | ├── test | ├── test_labels -Put a text file under the dataset directory called "class_dict.csv" which contains the list of classes along with the R, G, B colour labels to visualize the segmentation results. This kind of dictionairy is usually supplied with the dataset. Here is an example for the CamVid dataset: +Put a text file under the dataset directory called "class_dict.csv" which contains the list of classes along with the R, G, B colour labels to visualize the segmentation results. This kind of dictionary is usually supplied with the dataset. Here is an example for the CamVid dataset: ``` name,r,g,b @@ -191,7 +191,7 @@ optional arguments: augmentation --brightness BRIGHTNESS Whether to randomly change the image brightness for - data augmentation. Specifies the max bightness change + data augmentation. Specifies the max brightness change as a factor between 0.0 and 1.0. For example, 0.1 represents a max brightness change of 10% (+-). --rotation ROTATION Whether to randomly rotate the image for data