Merge pull request CSAILVision#245 from davidbau/tutorial

Tutorial fixes

Author: hangzhaomit

README.md

@@ -7,6 +7,8 @@ https://github.com/CSAILVision/sceneparsing
 
 If you simply want to play with our demo, please try this link: http://scenesegmentation.csail.mit.edu You can upload your own photo and parse it!
 
+[You can also use this colab notebook playground here](https://colab.research.google.com/github/CSAILVision/semantic-segmentation-pytorch/blob/master/notebooks/DemoSegmenter.ipynb) to tinker with the code for segmenting an image.
+
 All pretrained models can be found at:
 http://sceneparsing.csail.mit.edu/model/pytorch
 

notebooks/DemoSegmenter.ipynb

@@ -9,9 +9,9 @@
     "This is a notebook for running the benchmark semantic segmentation network from the the [ADE20K MIT Scene Parsing Benchchmark](http://sceneparsing.csail.mit.edu/).\n",
     "\n",
     "The code for this notebook is available here\n",
-    "https://github.com/davidbau/semantic-segmentation-pytorch/tree/tutorial/notebooks\n",
+    "https://github.com/CSAILVision/semantic-segmentation-pytorch/tree/master/notebooks\n",
     "\n",
-    "It can be run on Colab at this URL https://colab.research.google.com/github/davidbau/semantic-segmentation-pytorch/blob/tutorial/notebooks/DemoSegmenter.ipynb"
+    "It can be run on Colab at this URL https://colab.research.google.com/github/CSAILVision/semantic-segmentation-pytorch/blob/master/notebooks/DemoSegmenter.ipynb"
    ]
   },
   {
@@ -34,8 +34,8 @@
     "!(stat -t /usr/local/lib/*/dist-packages/google/colab > /dev/null 2>&1) && exit \n",
     "pip install yacs 2>&1 >> install.log\n",
     "git init 2>&1 >> install.log\n",
-    "git remote add origin https://github.com/davidbau/semantic-segmentation-pytorch.git 2>> install.log\n",
-    "git pull origin tutorial 2>&1 >> install.log\n",
+    "git remote add origin https://github.com/CSAILVision/semantic-segmentation-pytorch.git 2>> install.log\n",
+    "git pull origin master 2>&1 >> install.log\n",
     "DOWNLOAD_ONLY=1 ./demo_test.sh 2>> install.log"
    ]
   },
@@ -157,7 +157,7 @@
     "\n",
     "The segmentation model is coded as a function that takes a dictionary as input, because it wants to know both the input batch image data as well as the desired output segmentation resolution.  We ask for full resolution output.\n",
     "\n",
-    "Then we use the previously-defined visualize_result function to render the semgnatioon map."
+    "Then we use the previously-defined visualize_result function to render the segmentation map."
    ]
   },
   {

notebooks/README.md

@@ -0,0 +1,12 @@
+Semantic Segmentation Demo
+==========================
+
+This directory contains a notebook for demonstrating the benchmark
+semantic segmentation network from the the ADE20K MIT Scene Parsing
+Benchchmark.
+
+It can be run on Colab at
+[this URL](https://colab.research.google.com/github/CSAILVision/semantic-segmentation-pytorch/blob/master/notebooks/DemoSegmenter.ipynb)
+or on a local Jupyter notebook.
+
+If running locally, run the script `setup_notebooks.sh` to start.