renumics/cifar100-enriched

Dataset Overview

• Dataset Name: CIFAR-100-Enriched
• Dataset Version: Enriched version provided by Renumics
• Dataset Category: Image Classification
• Dataset Size: 10K < n < 100K
• Dataset Tags: image classification, cifar-100, cifar-100-enriched, embeddings, enhanced, spotlight, renumics
• Language: English
• Multilinguality: Monolingual
• Annotation Creators: Crowdsourced
• Language Creators: N/A

Detailed Description

• Summary: This dataset is an enriched version of CIFAR-100, adding image embeddings and other application‑specific enrichment to help the machine‑learning community better understand and apply data‑center AI principles.
• Content: Contains 60,000 32×32 colour images across 100 classes, with 600 images per class. The dataset includes 50,000 training images and 10,000 test images. Each image has a "fine" label and a "coarse" label.
• Enrichment: Enhanced with image embeddings generated by a Vision Transformer.
• Structure:
  • Data Instances: Each instance includes image path, raw image, fine label, coarse label, predictions, etc.
  • Fields: Image, raw image, labels, predictions, embeddings, etc.
  • Splits: 50,000 training images, 10,000 test images.

Usage

• Exploration: Use Renumics Spotlight to quickly analyze and explore the dataset.
• Supported Tasks: Image classification – assign each image to one of 100 categories.
• Language: English labels.

Creation

• Source: CIFAR-10 and CIFAR-100 are subsets of the 80 million tiny images dataset, collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.
• Contributors: Alex Krizhevsky, Vinod Nair, Geoffrey Hinton, Renumics GmbH.

Citation

@article{krizhevsky2009learning, added-at = {2021-01-21T03:01:11.000+0100}, author = {Krizhevsky, Alex}, biburl = {https://www.bibsonomy.org/bibtex/2fe5248afe57647d9c85c50a98a12145c/s364315}, interhash = {cc2d42f2b7ef6a4e76e47d1a50c8cd86}, intrahash = {fe5248afe57647d9c85c50a98a12145c}, pages = {32--33}, timestamp = {2021-01-21T03:01:11.000+0100}, title = {Learning Multiple Layers of Features from Tiny Images}, url = {https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf}, year = 2009 }