diff --git a/.github/ISSUE_TEMPLATE/issue-template.md b/.github/ISSUE_TEMPLATE/issue-template.md
new file mode 100644
index 00000000..3d5643e9
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/issue-template.md
@@ -0,0 +1,18 @@
+---
+name: Issue template
+about: Ask for clarification, report bug or request new features
+title: "[Summary of the question]"
+labels: ''
+assignees: ''
+
+---
+
+Before posting an issue:
+
+- Check [here](https://github.com/dBeker/Faster-RCNN-TensorFlow-Python3/issues) if anyone already posted the question
+- Make sure you followed all steps in the [README](https://github.com/dBeker/Faster-RCNN-TensorFlow-Python3/blob/master/README.md)
+- Choose a clear and appropriate title (remember: you don't have to paste the error code nor to write a too long title, only to sum it up). **Do not use CAPS LOCK nor exclamation point**.
+
+Information to provide:
+- If an error is encountered, the traceback
+- If a new feature/information is request, be straightforward and complete
diff --git a/README.md b/README.md
index 3b7e6ef7..62d353e9 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,10 @@
 # tf-faster-rcnn
 Tensorflow Faster R-CNN for Windows and Linux by using Python 3
 
-This is the branch to compile Faster R-CNN on Windows and Linux. It is heavily inspired by the great work done [here](https://github.com/smallcorgi/Faster-RCNN_TF) and [here](https://github.com/rbgirshick/py-faster-rcnn). I have not implemented anything new but I fixed the implementations for Windows, Linux and Python 3.
+This is the branch to compile Faster R-CNN on Windows and Linux. It is heavily inspired by the great work done [here](https://github.com/smallcorgi/Faster-RCNN_TF) and [here](https://github.com/rbgirshick/py-faster-rcnn).
 
 Currently, this repository supports Python 3.5, 3.6 and 3.7. Thanks to @morpheusthewhite
 
-### PLEASE BE AWARE: I do not have time or intention to fix all the issues for this branch as I do not use it commercially. I created this branch just for fun. If you want to make any commitment, it is more than welcome. Tensorflow has already released an object detection api. Please refer to it. https://github.com/tensorflow/models/tree/master/research/object_detection
-
-### If you find a solution to an existing issue in the code, please send a PR for it.
-
-### Also, instead of trying to deal with Tensorflow, use Chainer. It is ready to be used with all the common models https://github.com/chainer/chainercv & https://github.com/chainer/chainer . I can reply all of your questions about Chainer
-
 # How To Use This Branch
 1. Install tensorflow, preferably GPU version. Follow [instructions]( https://www.tensorflow.org/install/install_windows). If you do not install GPU version, you need to comment out all the GPU calls inside code and replace them with relavent CPU ones.
 
@@ -26,7 +20,7 @@ Run `python setup.py build_ext install`
 Go to ./lib/utils and run `python setup.py build_ext --inplace`
 
 5. Follow [these instructions](https://github.com/rbgirshick/py-faster-rcnn#beyond-the-demo-installation-for-training-and-testing-models) to download PyCoco database.
-I will be glad if you can contribute with a batch script to automatically download and fetch. The final structure has to look like  
+I would be glad if you can contribute with a batch script to automatically download and fetch. The final structure has to look like  
 `data\VOCDevkit2007\VOC2007`  
 
 1. Download pre-trained VGG16 from [here](http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz) and place it as `data\imagenet_weights\vgg16.ckpt`.