renumics/cifar100-enriched
The CIFAR-100-Enriched dataset is an augmented version of the original CIFAR-100 dataset, comprising 60,000 color images of 32 × 32 pixels across 100 classes, with 600 images per class. In addition to fine‑grained labels (specific categories), it includes coarse‑grained labels (super‑categories). Augmentation includes image embeddings generated by a Vision Transformer, facilitating data analysis and model training. The dataset aims to promote data‑driven AI principles and advance research in image classification.
Dataset description and usage context
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 }
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