Update README.md

README.md

@@ -1,21 +1,45 @@
-# Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics [[Project Page]](https://sjenni.github.io/LCI/) 
+## Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics [[Project Page]](https://sjenni.github.io/LCI/) 
 
-This repository contains demo code of our CVPR2020 [paper](https:https://arxiv.org/abs/2004.02331). 
-It contains code for the training and evaluation on the STL-10 dataset. 
+[Simon Jenni](https://sjenni.github.io), [Hailin Jin](https://sites.google.com/view/hailinjin), and [Paolo Favaro](http:https://www.cvg.unibe.ch/people/favaro).  
+In [CVPR](https://arxiv.org/abs/2004.02331), 2020.
 
-*Training and evaluation on ImageNet is coming soon!*
+![Model](https://sjenni.github.io/LCI/assets/model_LCI.png)
+
+
+This repository contains code for self-supervised pre-training and supervised transfer learning on the STL-10 dataset.
+
+***Training and evaluation on ImageNet is coming soon!***
 
 ## Requirements
-The code is based on Python 3.7 and tensorflow 1.15.
+The code is based on Python 3.7 and tensorflow 1.15. 
 
 ## How to use it
 
 ### 1. Setup
 
-- Set the paths to the data and log directories in **constants.py**.
-- Run **init_datasets.py** to download and convert the STL-10 dataset.
+- Set the paths to the data and log directories in [constants.py](constants.py).
+- Run [init_datasets.py](init_datasets.py) to download and convert the STL-10 dataset to the TFRecord format:
+```
+python init_datasets.py
+```
 
 ### 2. Training and evaluation 
 
-- To train and evaluate a transformation classifier on STL-10 run **run_stl10.py**.
+- To train and evaluate a transformation classifier on STL-10 execute [run_stl10.py](run_stl10.py). An example usage could look like this: 
+```
+python run_stl10.py --tag='test' --num_gpus=1
+```
+
+## Citation
+
+If you find this repository useful for your research, please use the following.
 
+```
+@inproceedings{jenni2020steering,
+ title={Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics},
+ author={Jenni, Simon and Jin, Hailin and Favaro, Paolo},
+ booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+ pages={6408--6417},
+ year={2020}
+}
+```