Expand readme

README.md

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 # Colorization of Grey Images by applying a Convolutional Autoencoder on the Jetson Nano
+## by Dennis Konkol and Tim Niklas Witte
+
+This repository contains an pretrainied convolutional autoencoder for colorization of grey images.
+The live camera stream will be colorizatized in real time.
+The architecture of the ANN is optimized to run on the Jetson Nano.
+In total, 10 FPS can be archived on this embedded GPU.
 
 ![Example Video](videoPresentation.gif)
+
+## Requirements
+
+- TensorFlow 2
+- OpenCV 3.3.1
+
+## Usage
+
+### Training
+
+Run `Training.py` to start the training of the model.
+Each epoch the weights are stored into `./saved_models`.
+Besides, in `./test_logs` are the corresponding trainings statistics (train and test loss and also a batch of colorized test images) logged.
+
+```bash
+python3 Training.py
+```
+
+### Live colorization
+
+The launch of `live_recolor_plot.py` opens a window as shown in the GIF at the start of this README.
+
+```bash
+python3 live_recolor.py
+```
+
+It has the following structure:
+
+```bash
+(1) | (2) | (3) | (4)
+
+(1) = live RGB camera image
+(2) = live grey camera image
+(3) = live colorized image
+```
+
+To get also displayed a loss plot (mean squared error between `(1)` and `(3)`),
+run `live_recolor_plot.py` instead.
+The loss plot is presented right from `(3)`.
+
+```bash
+python3 live_recolor_plot.py
+```
+
+### Pretrainied Model