“Unleash the Power of AI: Revolutionize Overhead Imagery with Self-Supervised Vision Transformers on Amazon SageMaker!”

Introduction:

In this guest blog post co-written with Ben Veasey, Jeremy Anderson, Jordan Knight, and June Li from Travelers, we explore the use of self-supervised learning (SSL) for training machine learning models to interpret satellite and aerial images. SSL involves training models on unlabeled image data, which allows them to learn image representations that can be applied to various tasks. We demonstrate how to train self-supervised vision transformers on overhead imagery using Amazon SageMaker. This approach has led to improved model performances within the Travelers Data & Analytics space. We provide a step-by-step guide on preparing the dataset, training the models, and transferring the learned representations to downstream tasks.

Full Article: “Unleash the Power of AI: Revolutionize Overhead Imagery with Self-Supervised Vision Transformers on Amazon SageMaker!”

Transforming Satellite and Aerial Images for Machine Learning Models: The Story of Travelers and Amazon SageMaker

Satellite and aerial images have the power to unravel a multitude of complex problems in various fields, from precision agriculture to insurance risk assessment. They provide valuable insights into urban development and disaster response. However, one significant challenge in utilizing these images for machine learning (ML) models is the extensive effort and time required for human annotation.

Travelers, in collaboration with the Amazon Machine Learning Solutions Lab, sought a solution to this bottleneck by leveraging self-supervised learning (SSL). By training on vast amounts of unlabeled image data, self-supervised models can learn image representations that can be applied to downstream tasks like image classification or segmentation. This technique enables the models to generalize well to unseen data, reducing the need for labeled data to build robust models.

In this blog post, we present an innovative framework developed by Travelers and the Amazon Machine Learning Solutions Lab for training self-supervised vision transformers on overhead imagery using Amazon SageMaker. This framework aims to support and enhance the use of aerial imagery models within Travelers’ Data & Analytics space, ultimately improving model performance.

Overview of the Solution: Pre-training Vision Transformers

The solution involves a two-step process for pre-training vision transformers and transferring them to supervised downstream tasks. By following this approach, Travelers successfully trained self-supervised models using the DINO algorithm and the SageMaker distributed data parallel library (SMDDP) to distribute the data across multiple GPU instances. The result is a set of image representations that can be effectively utilized for various downstream tasks.

Preparing the BigEarthNet-S2 Dataset

To illustrate the solution, Travelers used the BigEarthNet-S2 dataset, which consists of 590,325 multispectral images collected by the Sentinel-2 satellite. These images document the land cover of ten European countries over a specific period. Each image is annotated with 19 different labels indicating the types of land cover, such as forests or pastures.

To prepare the dataset for DINO training, Travelers downloaded the dataset and processed it using the BigEarthNet Common and BigEarthNet GDF Builder helper packages. The metadata files were converted into a geopandas Parquet file, facilitating statistical analysis and data loading during training. The dataset was then uploaded to an Amazon Simple Storage Service (Amazon S3) bucket.

Training DINO Models with SageMaker

Now that the dataset is ready, Travelers moves on to train DINO models on the BigEarthNet-S2 dataset using Amazon SageMaker. The DINO algorithm involves passing different global and local crops of an input image to student and teacher networks, with the student network learning to match the output of the teacher network. The weights of the student and teacher networks are connected by an exponential moving average (EMA).

Travelers made a couple of modifications to the original DINO code to cater to the specific requirements of the BigEarthNet-S2 dataset. They created a custom PyTorch dataset class that loads the dataset images and applies transformations. This class supports multi-label image datasets and one-hot encodes the land cover labels. Additionally, they added support for the SageMaker distributed data parallel library (SMDDP) to train models on multiple GPUs or instances.

The Results: Enhanced Model Performances

By implementing this framework, Travelers achieved improved model performances within their Data & Analytics space. The self-supervised vision transformers trained on the BigEarthNet-S2 dataset proved to be effective in various downstream tasks, showcasing the potential of utilizing self-supervised learning for satellite and aerial imagery analysis.

Wrap Up

Satellite and aerial images present opportunities for addressing complex challenges in diverse domains. Travelers, in partnership with the Amazon Machine Learning Solutions Lab, developed a cutting-edge solution using self-supervised learning and Amazon SageMaker to train vision transformers on overhead imagery. This collaboration resulted in enhanced model performances and demonstrated the value of leveraging self-supervised learning for analyzing satellite and aerial images.

If you’re interested in exploring similar approaches for your own projects, consider leveraging self-supervised learning and the power of Amazon SageMaker to unlock the potential hidden in satellite and aerial imagery.

Summary: “Unleash the Power of AI: Revolutionize Overhead Imagery with Self-Supervised Vision Transformers on Amazon SageMaker!”

Satellite and aerial images are valuable for various applications such as precision agriculture and insurance risk assessment. However, training machine learning models to interpret these images can be time-consuming and expensive, as it requires human annotation. One solution is self-supervised learning (SSL), which involves training models on large amounts of unlabeled data. SSL models learn image representations that can be used for downstream tasks like image classification. This approach reduces the need for labeled data and produces representations that generalize well to new data. In this article, we explore how to train self-supervised vision transformers on overhead imagery using Amazon SageMaker, a collaboration between Travelers and the Amazon Machine Learning Solutions Lab. We provide a walkthrough of the process and discuss the results achieved within Travelers’ Data & Analytics space.

Train Self-Supervised Vision Transformers on Overhead Imagery with Amazon SageMaker

About

Amazon SageMaker is a powerful machine learning platform that allows you to train and deploy models with ease. It provides a comprehensive set of tools for building, training, and deploying machine learning models at scale.

Introduction

Self-supervised vision transformers have gained popularity in recent years due to their ability to learn representations from unlabeled data. This technique is particularly useful for overhead imagery analysis, where labeled data is often scarce or costly to obtain.

Benefits of Self-Supervised Vision Transformers

• Efficiently utilize unlabeled data
• Reduce dependency on labeled data
• Improve model performance

Getting Started with Amazon SageMaker

To train self-supervised vision transformers on overhead imagery using Amazon SageMaker, follow the steps below:

Step 1: Set up your workspace

Before you can start training, you need to set up a SageMaker workspace. Follow the instructions provided by Amazon to create your workspace.

Step 2: Prepare your data

Organize your overhead imagery dataset according to the guidelines provided by Amazon. Ensure that your unlabeled data is properly formatted and stored in a compatible format.

Step 3: Configure your training job

Create a training job configuration file that specifies the details of your self-supervised vision transformer model, hyperparameters, and other relevant settings.

Step 4: Start the training job

Submit your training job using the Amazon SageMaker console or API. Monitor the progress and make adjustments as needed.

FAQs

Q: Can I use labeled data along with unlabeled data to train self-supervised vision transformers?

A: Yes, you can augment your unlabeled dataset with a small portion of labeled data to improve the performance of your model.

Q: What image formats are compatible with Amazon SageMaker?

A: Amazon SageMaker supports various image formats such as JPEG, PNG, and TIFF. Make sure your data is in one of these formats before training.

Q: Can I fine-tune a pre-trained self-supervised vision transformer model?

A: Absolutely. Fine-tuning allows you to adapt a pre-trained model to a specific task or domain by training it on a smaller labeled dataset.

Q: Is there a limit to the number of images I can include in my dataset?

A: Amazon SageMaker does not impose a hard limit on the number of images in your dataset. However, consider the available storage and computational resources when handling large datasets.

Q: Can I use Amazon SageMaker for real-time inference with my trained model?

A: Yes, Amazon SageMaker provides a deployment feature that allows you to deploy your trained model for real-time inference, enabling seamless integration into your applications.

Q: Where can I find more resources and documentation on using Amazon SageMaker?

A: Visit the Amazon SageMaker documentation and resources page for comprehensive guides, tutorials, and examples to help you make the most of the platform.