diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index c6a78af..c880346 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -2,19 +2,19 @@
 
 **Please, follow these steps**
 
-## Step 1: Forking and Installing vision
+## Step 1: Forking and Installing quickvision
 
 ​1. Fork the repo to your own github account. Click the Fork button to
 create your own repo copy under your GitHub account. Once forked, you're
 responsible for keeping your repo copy up-to-date with the upstream
 quickvision repo.
 
-​2. Download a copy of your remote username/quickvision repo to your
+​2. Download a copy of your remote `<username>/quickvision` repo to your
 local machine. This is the working directory where you will make
 changes:
 
 ```bash
-$ git clone https://github.com/Quick-AI/quickvision
+$ git clone https://github.com/<username>/quickvision
 ```
 
 3.  Install the requirements. You may use miniconda or conda as well.
@@ -32,7 +32,7 @@ $ python setup.py develop
 ## Step 2: Set up upstream repo
 
 1.  Set the upstream to sync with this repo. This will keep you in sync
-    with quickvision easily.
+    with `quickvision` easily.
 
 ```bash
 $ git remote add upstream https://github.com/Quick-AI/quickvision
@@ -48,10 +48,10 @@ $ git pull upstream master
 ## Step 3: Creating a new branch
 
 ```bash
-$ git checkout -b feature-name
+$ git checkout -b <feature-name>
 $ git branch
  master 
- * feature-name: 
+ * <feature-name>: 
 ```
 
 ## Step 4: Make changes, and commit your file changes
@@ -59,7 +59,7 @@ $ git branch
 Stage and commit your changes.
 
 ```
-git add path/to/file.md
+git add .
 git commit -m "Your meaningful commit message for the change."
 ```
 
@@ -70,10 +70,10 @@ Add more commits, if necessary.
 #### 1. Create a pull request git
 
 Upload your local branch to your remote GitHub repo
-(github.com/username/quickvision)
+(`github.com/<username>/quickvision`)
 
 ```bash
-git push
+git push origin <feature-name>
 ```
 
 After the push completes, a message may display a URL to automatically
@@ -81,26 +81,24 @@ submit a pull request to the upstream repo. If not, go to the
 quickvision main repo and GitHub will prompt you to create a pull
 request.
 
-#### 2. Confirm PR was created:
+#### 2. Confirm PR was created
 
 Ensure your PR is listed
 [here](https://github.com/Quick-AI/quickvision/pulls)
 
 3.  Updating a PR:
 
-Same as before, normally push changes to your branch and the PR will get
-automatically updated.
+    Same as before, normally push changes to your branch and the PR will get automatically updated.
 
-```bash
-git commit -m "updated the feature"
-git push origin <enter-branch-name-same-as-before>
-```
+    ```bash
+    git commit -m "Updated the feature"
+    git push origin <enter-branch-name-same-as-before>
+    ```
 
 * * * * *
 
 ## Reviewing Your PR
 
-Maintainers and other contributors will review your pull request. Please
-participate in the discussion and make the requested changes. When your
-pull request is approved, it will be merged into the upstream
-quickvision repo.
+Maintainers and other contributors will review your pull request.
+Please participate in the discussion and make the requested changes.
+When your pull request is approved, it will be merged into the upstream quickvision repo.
diff --git a/README.md b/README.md
index 0260e7d..d8655e5 100644
--- a/README.md
+++ b/README.md
@@ -27,32 +27,32 @@
 ### Install Quickvision
 
 - Install from PyPi.
-- Current stable release 0.1 needs PyTorch 1.7 and torchvision 0.8.1.
+- Current stable `release 0.1` needs `PyTorch 1.7` and `torchvision 0.8.1`.
 
-```
-pip install quickvision
-```
+    ```
+    pip install quickvision
+    ```
 
 ## What is Quickvision?
 
 - Quickvision makes Computer Vision tasks much faster and easier with PyTorch.
 
-It provides: -
+    It provides: -
 
-1. Easy to use torch native API, for `fit()`, `train_step()`, `val_step()` of models.
-2. Easily customizable and configurable models with various backbones.
-3. A complete torch native interface. All models are `nn.Module` all the training APIs are optional and not binded to models.
-4. A lightning API which helps to accelerate training over multiple GPUs, TPUs.
-5. A datasets API to common data format very easily and quickly to torch formats.
-6. A minimal package, with very low dependencies.
+    1. Easy to use PyTorch native API, for `fit()`, `train_step()`, `val_step()` of models.
+    2. Easily customizable and configurable models with various backbones.
+    3. A complete PyTorch native interface. All models are `nn.Module`, all the training APIs are optional and not binded to models.
+    4. A lightning API which helps to accelerate training over multiple GPUs, TPUs.
+    5. A datasets API to convert common data formats very easily and quickly to PyTorch formats.
+    6. A minimal package, with very low dependencies.
 
-- Train your models faster. Quickvision has already implmented the long learning in torch.
+- Train your models faster. Quickvision has already implemented the long learning in PyTorch.
 
-## Quickvision is just Torch!!
+## Quickvision is just PyTorch!!
 
-- Quickvision does not make you learn a new library. If you know PyTorch you are good to go!!!
-- Quickvision does not abstract any code from torch, nor implements any custom classes over it.
-- It keeps the data format in `Tensor` only. You don't need to convert it.
+- Quickvision does not make you learn a new library. If you know PyTorch, you are good to go!!!
+- Quickvision does not abstract any code from PyTorch, nor implements any custom classes over it.
+- It keeps the data format in `Tensor` so that you don't need to convert it.
 
 ### Do you want just a model with some backbone configuration?
 
@@ -66,4 +66,4 @@ It provides: -
 ### Do you want multi GPU training but worried about model configuration?
 
 - Just subclass the PyTorch Lightning model! 
-- Implement the `train_step`, `val_step`.
+- Implement the `train_step()`, `val_step()`.