DEIC Benchmark (Data-Efficient Image Classification Benchmark)

Introduced by Brigato et al. in Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification

DEIC is a benchmark for measuring the data efficiency of models in the context of image classification. It is composed of 6 datasets that contain a small number of training samples per class (i.e., 30 < x < 80). It covers multiple image domains (i.e., natural images, fine-grained recognition, medical images, remote sensing, handwriting recognition) and data types (i.e., RGB, grayscale, multi-spectral).

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Benchmarks

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Task	Dataset Variant	Best Model
Small Data Image Classification	DEIC Benchmark	Harmonic Networks
Small Data Image Classification	ciFAIR-10 50 samples per class	ChimeraMix+AutoAugment
Small Data Image Classification	CUB-200-2011, 30 samples per class	GLICO
Small Data Image Classification	ImageNet 50 samples per class	Harmonic Networks
Small Data Image Classification	EuroSAT 50 samples per class	Harmonic Networks