Object-Centric Learning with Slot Attention

Related tags

Deep Learningslot_attention
Overview

Slot Attention

This is a re-implementation of "Object-Centric Learning with Slot Attention" in PyTorch (https://arxiv.org/abs/2006.15055).

Outputs of our slot attention model. This image demonstrates the models ability to divide objects (or parts of objects) into slots.

Requirements

  • Poetry
  • Python >= 3.8
  • PyTorch >= 1.7.1
  • Pytorch Lightning >= 1.1.4
  • CUDA enabled computing device

Note: the model was run using a Nvidia Tesla V100 16GB GPU.

Getting Started

Run run.sh to get started. This script will install the dependencies, download the CLEVR dataset and run the model.

Usage

python slot_attention/train.py

Modify SlotAttentionParams in slot_attention/train.py to modify the hyperparameters. See slot_attenion/params.py for the default hyperparamters.

Logging

To log outputs to wandb, run wandb login YOUR_API_KEY and set is_logging_enabled=True in SlotAttentionParams.

Acknowledgements

Special thanks to the original authors of the paper: Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf.

Owner
Untitled AI
We're investigating the fundamentals of learning across humans and machines in order to create more general machine intelligence.
Untitled AI
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