--- a
+++ b/README.md
@@ -0,0 +1,43 @@
+# Segmentation of nuclei using DSB-2018 top-1 neural network model
+Based on [selimsef/dsb2018_topcoders](https://github.com/selimsef/dsb2018_topcoders/)
+
+For comparison of Data Science Bowl 2018 best segmentation models see [Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl, Juan C. Caicedo et al](https://www.nature.com/articles/s41592-019-0612-7).  
+
+## Installation
+1. Clone this repository
+
+```
+git clone https://github.com/yozhikoff/segmentation.git
+```
+
+2. Download [this](https://www.dropbox.com/s/qvtgbz0bnskn9wu/dsb2018_topcoders.zip?dl=0) and extract it to the
+segmentation folder, replace all existing files using `Ay` keys when unzip asks about it. Note that you need to export to `/repo/segmentation/dsb2018_topcoders` withing the repo.
+
+```
+wget https://www.dropbox.com/s/qvtgbz0bnskn9wu/dsb2018_topcoders.zip?dl=1 dsb2018_topcoders.zip # note dl=1
+unzip /path/to/zip/dsb2018_topcoders.zip -d /path/to/repo/segmentation/dsb2018_topcoders #type "Ay" when it asks about conflicts
+```
+
+3. Go to the segmentation folder and reset git files
+
+```shell script
+cd /path/to/repo/segmentation
+git reset --hard
+```
+
+4. Create new conda env
+``` 
+conda create -n seg python=3.6.9 -y
+conda activate seg
+``` 
+5) Install packages via conda and pip, simply (inside your conda env!)
+
+```
+sh ./install.sh
+```
+6) Test your installation using
+```
+python run_test.py
+```
+
+You can also try `example_notebook.ipynb` if you want to see usage details.