Fill out README

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> Predicting film genres from their posters with Tensorflow.js
## Dataset
The dataset used with this demo can be found here:
To use:
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](
- [NPM]( (installed by default with Node.js)
- A relatively decent CPU
- Basic knowledge of the command-line / terminal
## Installation
First, clone this git repo:
node src/index.mjs train --input datasets/posters-256/ --output output/
node src/index.mjs predict --input datasets/posters-256/validate/25410,Comedy,Family,Drama.jpg --ai-model output/checkpoints/49 2>/dev/null
git clone
cd film-poster-genres
Then, install the dependencies:
npm install
## Usage
### Training
To train a new model:
node src/index.mjs train --input path/to/dataset_dir --output path/to/output_dir
The output directory will look like this:
+ output_dir/
+ 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:
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]( of the license if you're interested.