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A machine learning contest to predict the behavior of catz

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# Catz Benchmark

This is the starter repository for the Weights & Biases community benchmark for predicting the behavior of catz from a dataset of cat GIFs.

## Getting started

1. Be sure to [sign up](https://app.wandb.ai/login?signup=true) for W&B.
2. Clone this repository: `git clone https://github.com/wandb/catz.git`
3. Run `pip install -U -r requirements.txt` to install requirements.
4. Run `python train.py` to train the baseline model. Modify this file and the data pipeline (or write your own scripts and create different model architectures!) to get better results.
5. Submit your results to the [benchmark](https://app.wandb.ai/wandb/catz/benchmark).

![catz](https://www.americanhumane.org/app/uploads/2016/08/animals-cats-cute-45170-min-1024x569.jpg)

## The dataset

The dataset is comprised of sequences extracted from GIFs of cats thanks to [GIPHY](https://giphy.com)! Each cat has its own directory, which contains a sequence of 6 images. There are 6421 sequences in the training set and 1475 in the test set. Each image is 96x96 pixels.

![catz](https://storage.googleapis.com/wandb/catz.jpg)

## The goal

The goal is to predict the 6th frame given 5 consecutive previous frames.

## Evaluation

We use a [perceptual distance](https://www.compuphase.com/cmetric.htm) metric (val_perceptual_distance) on the validation set to rank results (lower values are better).

## Submitting your results

You can submit your best runs to our [benchmark](https://app.wandb.ai/wandb/catz/benchmark). More specifically, go the "Runs" table in the "Project workspace" tab of your project.
Hover over the run's name, click on the three-dot menu icon that appears to the left of the name, and select "Submit to benchmark".

## Things to try

- Use an RNN
- Different loss functions
- Data augmentation

## Qualcomm

Participating from Qualcomm? See [this README](QUALCOMM.md) for more details.

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