Introduction to Masked Autoencoders As Spatiotemporal Learners (mae_st)
Overview
The "Masked Autoencoders As Spatiotemporal Learners" project, also referred to as MAE, is an innovative implementation crafted using PyTorch and GPU technology. It revisits the concepts presented in a paper authored by Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, and Kaiming He in the year 2022. The project provides capabilities to process and analyze video data through a framework that effectively learns about time-based changes and spatial relationships using a masked autoencoder approach.
Key Features
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Visualization Demo: The project provides a captivating visualization demo that displays MAE output with different mask rates—95% and 98%—on the same video. This demonstration highlights how the model can maintain data interpretation even with significant portions of the input data obscured.
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Pre-trained Checkpoints: Users have access to pre-trained checkpoints optimized for different Kinetics datasets, such as Kinetics-400, Kinetics-600, and Kinetics-700. This ensures that the model is suitable for a wide range of applications and tasks.
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Fine-tuning and Testing: Apart from the pre-trained models, the project includes code for fine-tuning and testing, aiding users to adjust the model to specific requirements or datasets.
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Pre-training Capabilities: For those interested in training models from scratch, there are guidelines available to facilitate this process.
Practical Application
This project integrates with [PySlowFast], offering an alternative implementation that supports downstream evaluation on datasets such as AVA and SSv2. The checkpoint files provided within the project are essential for working with these complex datasets.
Interactive Exploration
To allow for interactive exploration, there is a demo available through a Colab notebook that doesn't require a GPU. This makes it more accessible for individuals who may not have high-end computational resources.
Getting Started
The underlying base of this project, known as the [MAE repo], offers detailed information about installation and preparation, ensuring that users can set up their environment correctly. Additionally, it is constructed to work with the PyTorch version 1.8.1 and above, requiring minor adjustments to the timm library to ensure compatibility.
License
The project is shared under the CC-BY-NC 4.0 license, allowing users to share, use, and adapt the work provided it is not for commercial purposes and proper credit is given.
This detailed overview of the MAE project gives insight into its capabilities and applications, guiding users through its features and usage.