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Predicting film genres from their posters with Tensorflow.js
The example code & slide deck for a talk I gave on getting started with AI. A link to the unlisted YouTube video is available upon request (because it contains my face, and this is a public repo) - see my website for ways to get in touch.
For advanced users, see also the
post-talk-improvements branch, which contains a number of adjustments and changes to this codebase. These changes improve the model, but make it more complex to understand and hence are kept in a separate branch to allow regular users to look at the simpler / easier to understand codebase.
The dataset used with this demo can be found here:
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:
System / User Requirements
- NPM (installed by default with Node.js)
- A relatively decent CPU
- Basic knowledge of the command-line / terminal
First, clone this git repo:
git clone https://git.starbeamrainbowlabs.com/Demos/film-poster-genres.git cd film-poster-genres
Then, install the dependencies:
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/ + metrics.stream.json + checkpoints/ + 0/ + 1/ + 2/ + 3/
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.
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).
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.