Image classification - fast.ai datasets

Stream data with DDA:

from dagshub.streaming import DagsHubFilesystem

fs = DagsHubFilesystem(".", repo_url="https://dagshub.com/DagsHub-Datasets/fast-ai-imageclas-dataset")

fs.listdir("s3://fast-ai-imageclas")

Description:

Some of the most important datasets for image classification research, including CIFAR 10 and 100, Caltech 101, MNIST, Food-101, Oxford-102-Flowers, Oxford-IIIT-Pets, and Stanford-Cars. This is part of the fast.ai datasets collection hosted by AWS for convenience of fast.ai students. See documentation link for citation and license details for each dataset.

Contact:

Some of the most important datasets for image classification research, including CIFAR 10 and 100, Caltech 101, MNIST, Food-101, Oxford-102-Flowers, Oxford-IIIT-Pets, and Stanford-Cars. This is part of the fast.ai datasets collection hosted by AWS for convenience of fast.ai students. See documentation link for citation and license details for each dataset.

Update Frequency:

As required

Managed By:

http://www.fast.ai/

Resources:

1. resource:
   • Description: Datasets
   • ARN: arn:aws:s3:::fast-ai-imageclas
   • Region: us-east-1
   • Type: S3 Bucket

Tags:

aws-pds, deep learning, computer vision, machine learning

Tools & Applications:

1. tools & applications:

   • Title: Oxford-IIIT Pet Image Classification on Amazon SageMaker

   • URL: https://github.com/aws/amazon-sagemaker-examples/blob/master/advanced_functionality/fastai_oxford_pets/fastai_lesson1_sagemaker_example.ipynb

   • AuthorName: AWS

   • AuthorURL: https://aws.amazon.com/sagemaker/