multimodal perceiver

Introduction

The Perceiver IO model, developed by DeepMind, is a transformer-based architecture designed for multimodal autoencoding, processing inputs such as images, audio, and class labels. It was introduced in the paper "Perceiver IO: A General Architecture for Structured Inputs & Outputs" by Jaegle et al. and is available for use on the Hugging Face platform.

Architecture

Perceiver IO leverages a transformer encoder that utilizes self-attention on a manageable set of latent vectors. It employs cross-attention with inputs to maintain efficiency regarding time and memory, irrespective of input size. The architecture allows for flexible decoding using decoder queries, which can output reconstructions of different modalities. This design enables the model to handle a variety of data types effectively.

Training

The model was trained on the Kinetics-700-2020 dataset, which includes videos from 700 classes. Training involved preprocessing video frames into patches and audio samples into vectors. Hyperparameters and further training specifics are detailed in Appendix F of the related research paper.

Guide: Running Locally

1. Clone the Repository: Begin by cloning the Perceiver repository from GitHub.
2. Install Dependencies: Ensure you have the necessary Python libraries installed, including Hugging Face's transformers library.
3. Download Pre-trained Model: Use the Hugging Face model hub to download the Perceiver IO model.
4. Run Inference: Load the model and test it on multimodal data using sample scripts from the repository.

For optimal performance, especially with large datasets, consider using cloud GPUs such as those provided by AWS or Google Cloud.

License

The Perceiver IO model is released under the Apache 2.0 License, allowing for widespread use and modification under the terms specified.