Image Classification On Cifar 10

Metrics

Percentage correct

Results

Performance results of various models on this benchmark

Model Name	Percentage correct	Paper Title	Repository
ABNet-2G-R2	95.900	ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel Realities
Beta-Rank	93.97	Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis
DCNN	89	ImageNet Classification with Deep Convolutional Neural Networks	-
VIT-L/16 (Spinal FC, Background)	99.05	Reduction of Class Activation Uncertainty with Background Information
LeViP	79.50	Vision Xformers: Efficient Attention for Image Classification
RReLU	88.8	Empirical Evaluation of Rectified Activations in Convolutional Network
ResNet-44 (Trainable Activations)	90.5	Trainable Activations for Image Classification
ResNet+ELU	94.4	Deep Residual Networks with Exponential Linear Unit
CeiT-S (384 finetune resolution)	99.1	Incorporating Convolution Designs into Visual Transformers
ViT-B/16 (PUGD)	99.13	Perturbated Gradients Updating within Unit Space for Deep Learning
ResNet-50-SAM	97.4	When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations
Evolution ensemble	95.6	Large-Scale Evolution of Image Classifiers
MomentumNet	95.18	Momentum Residual Neural Networks
UL-Hopfield (ULH)	83.1	Unsupervised Learning using Pretrained CNN and Associative Memory Bank	-
µ2Net (ViT-L/16)	99.49	An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
MCDNN	88.8	Multi-column Deep Neural Networks for Image Classification
Spectral Representations for Convolutional Neural Networks	91.4	Spectral Representations for Convolutional Neural Networks	-
WRN-28-10+AutoDropout+RandAugment	97.9	AutoDropout: Learning Dropout Patterns to Regularize Deep Networks
OnDev-LCT-2/3	86.04	OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning	-
Astroformer	99.12	Astroformer: More Data Might not be all you need for Classification

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