Fill out README

README.md

@@ -2,13 +2,83 @@
 
 > Predicting film genres from their posters with Tensorflow.js
 
+## Dataset
+The dataset used with this demo can be found here:
 
-To use:
+https://nextcloud.starbeamrainbowlabs.com/index.php/s/wmCdL4tX3HHGAFD
+
+The format is as follows:
+
+```
++ dataset_dir/
+	+ train/
+		+ 1234,Comedy,Fantasy.jpg
+		+ 4321,Western,Short,Drama.jpg
+		+ .....
+	+ validate/
+		+ 6789,Drama,Mystery,Thriller.jpg
+		+ 9876,History,Documentary,Animation.jpg
+		+ .....
+```
+
+The filenames of the images take the following format: `ID,GENRE_1,GENRE_2,GENRE_N.jpg`.
+
+## System / User Requirements
+ - [Node.js](https://nodejs.org/)
+ - [NPM](https://www.npmjs.com/) (installed by default with Node.js)
+ - A relatively decent CPU
+ - Basic knowledge of the command-line / terminal
+
+ 
+## Installation
+First, clone this git repo:
 
 ```bash
-node src/index.mjs train --input datasets/posters-256/ --output output/
+git clone https://git.starbeamrainbowlabs.com/Demos/film-poster-genres.git
-node src/index.mjs predict --input datasets/posters-256/validate/25410,Comedy,Family,Drama.jpg --ai-model output/checkpoints/49 2>/dev/null
+cd film-poster-genres
+```
+
+Then, install the dependencies:
+
+```bash
+npm install
+```
+
+## Usage
+
+### Training
+To train a new model:
+
+```bash
+node src/index.mjs train --input path/to/dataset_dir --output path/to/output_dir
+```
+
+The output directory will look like this:
+
+```
+ + output_dir/
+	+ metrics.stream.json
+	+ checkpoints/
+		+ 0/
+		+ 1/
+		+ 2/
+		+ 3/
 ```
 
 
-TODO: Fill out this README (and maybe move it to GitHub & make this repo a clone)
+## Predicting
+To make a prediction using an existing model:
+
+```bash
+node src/index.mjs predict --input path/to/image.jpg --ai-model path/to/checkpoint_dir/
+```
+
+The result will be written to the standard output. Extra debugging data is written to the standard error, but this can be ignored.
+
+
+## Contributing
+Contributions are very welcome! Git patches are preferred - I can move this repo to GitHub if that makes it easier. Please mention in your contribution that you release your work under the MPL-2.0 (see below).
+
+
+## Licence
+This code is released under the Mozilla Public License 2.0. The full license text is included in the `LICENSE` file in this repository. Tldr legal have a [great summary](https://tldrlegal.com/license/mozilla-public-license-2.0-(mpl-2)) of the license if you're interested.