docs: update and redesign README.md

README.md

@@ -1,39 +1,47 @@
-# Neural Style Transfer - CLI
+# Neural Style Transfer
 
 > Create **artificial artwork** by transferring the appearance of one image (ie a famous painting) to another user-supplied image (e.g. your favorite photo).
 
-| Build | Package | Containerization | Code Quality |
-|-------|---------|------------------|--------------|
-| ![CI Pipeline Status][ci-pipeline-status] | ![PyPI][pypi] ![Wheel][wheel] ![Python Versions][python-versions] ![Commits Since][commits-since] | ![Docker][docker] ![Image Size][image-size] | ![Codacy][codacy] ![Code Climate][code-climate] ![Maintainability][maintainability] ![Scrutinizer][scrutinizer] |
-
-
 **Documentation**: [https://boromir674.github.io/neural-style-transfer/](https://boromir674.github.io/neural-style-transfer/)
 
-| ![Demo Content Image](./tests/data/canoe_water_w300-h225.jpg) | + | ![Demo Style Image](./tests/data/blue-red_w300-h225.jpg) | = | ![Demo Gen Image](./tests/data/canoe_water_w300-h225.jpg+blue-red_w300-h225.jpg-100-demo-gui-run-1.png) |
+<div style="display: flex; align-items: center; margin-bottom: 25px;">
+    <img src="./tests/data/canoe_water_w300-h225.jpg" alt="Demo Content Image" style="vertical-align: middle;">
+    <span style="margin: 0 10px; font-size: 24px; vertical-align: middle;">+</span>
+    <img src="./tests/data/blue-red_w300-h225.jpg" alt="Demo Style Image" style="vertical-align: middle;">
+    <span style="margin: 0 10px; font-size: 24px; vertical-align: middle;">=</span>
+    <img src="./tests/data/canoe_water_w300-h225.jpg+blue-red_w300-h225.jpg-100-demo-gui-run-1.png" alt="Demo Gen Image" style="vertical-align: middle;">
+</div>
 
-Uses a Neural Style Transfer algorithm to transfer the appearance, which you can run through a CLI program.
+Uses a `Neural Style Transfer` algorithm to transfer the **style** (aka appearance), from one image, into the **contents** of another.
 
-`Neural Style Transfer` (NST) is an algorithm that applies the `style` of an image to the `contents` of another and produces a `generated` image. The idea is to find out how someone, with the `painting style` shown in one image, would depict the `contents` shown in another image.
 
-NST takes a `content` image (e.g., a picture taken with your camera) and a `style` image (e.g., a picture of a Van Gogh painting) and produces the `generated` image.
+`Neural Style Transfer (NST)` is an algorithm that applies the `style` of an image to the `contents` of another and produces a `generated` image.  
+The idea is to find out how someone, with the `painting style` shown in one image, would depict the `contents` shown in another image.
 
-This Python package runs a Neural Style Transfer algorithm on input `content` and `style` images to produce `generated` images.
-
-## Badges
+NST takes as INPUT a `Content` image (e.g. a picture taken with your camera) and a `Style` image (e.g. a Van Gogh painting) and `Generates` a new image.
 
 
 
 # Overview
+This project provides an `NST` algorithm through
+- the `artificial_artwork` **Python package** (aka module)
+- the `nst` **CLI**
+- the `boromir674/neural-style-transfer` **Docker image**
+
+<!-- This package exposes a configurable NST algorithm via a convenient CLI program. -->
 
-This package exposes a configurable NST algorithm via a convenient CLI program.
+| Build | Package | Containerization | Code Quality |
+|-------|---------|------------------|--------------|
+| ![CI Pipeline Status][ci-pipeline-status] | ![PyPI][pypi] ![Wheel][wheel] ![Python Versions][python-versions] ![Commits Since][commits-since] | ![Docker][docker] ![Image Size][image-size] | ![Codacy][codacy] ![Code Climate][code-climate] ![Maintainability][maintainability] ![Scrutinizer][scrutinizer] |
 
-Key features of the package:
 
-* Selection of style layers at runtime
-* Iterative Learning Algorithm using the VGG Deep Neural Network
-* Selection of iteration termination condition at runtime
-* Fast minimization of loss/cost function with parallel/multicore execution, using Tensorflow
-* Persisting of generated images
+## Features
+- VGG-19 Convolutional Neural Network, as `Image model`
+- Selection of `style layers` at runtime
+- Efficient Iterative Learning Algorithm, with `tensorflow`
+- Fast minimization of loss/cost function with parallel/multicore execution
+- Selection of Algorithm Termination Condition/Criteria, at runtime
+- Periodic persistance of `Generated image`, during Learning loop
 
 ## Quick-start