SPACENET DATASET HOW TO

Accelerating PyTorch Transformers with Intel Sapphire Rapids, part 2.

Accelerating PyTorch Transformers with Intel Sapphire Rapids, part 1.I referenced two Hugging Face blogs by Julien Simon he ran his tests on the AWS instance tal-16xl: SpaceNet 5 Winning Model Release: End of the Road.The SpaceNet 5 Baseline - Part 3: Extracting Road Speed Vectors from Satellite Imagery.The SpaceNet 5 Baseline - Part 2: Training a Road Speed Segmentation Model.The SpaceNet 5 Baseline - Part 1: Imagery and Label Preparation.

SPACENET DATASET HOW TO

I also highly recommend these articles for a detailed explanation of how to get started with the SpaceNet5 data: You can find the accompanying talk I gave on YouTube. In particular, I was able to piece together a workflow using the notebooks here. I adapted it for the 4th Gen Intel Xeon processors with PyTorch optimizations and Intel Extension for PyTorch optimizations. I borrowed heavily from the City-Scale Road Extraction from Satellite Imagery (CRESI) repository.

The code accompanying this article is available in the examples folder in the Intel Extension for PyTorch repository. I will walk you through the steps to work with a satellite image dataset called SpaceNet5 and how I optimized the code to make deep learning workloads feasible on CPUs just by flipping a few key switches.īefore we get started, some housekeeping… I trained an AI image segmentation model using PyTorch 1.13.1 (with ResNet34 + UNet architecture) to identify roads and speed limits from satellite images, all on the 4th Gen Intel® Xeon® Scalable processor. PyTorch delivers great CPU performance, and it can be further accelerated with Intel® Extension for PyTorch. Using Intel® Extension for PyTorch to Boost Image Processing Performance

Spacenet dataset