Image Classification On Mnist

Metrics

Percentage error

Results

Performance results of various models on this benchmark

Model Name	Percentage error	Paper Title	Repository
MCDNN	0.23	Multi-column Deep Neural Networks for Image Classification
SEER (RegNet10B)	0.58	Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Second Order Neural Ordinary Differential Equation	0.37	On Second Order Behaviour in Augmented Neural ODEs
FLSCNN	0.4	Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network	-
PCANet	0.6	PCANet: A Simple Deep Learning Baseline for Image Classification?
CNN+ Wilson-Cowan model RNN	-	Learning in Wilson-Cowan model for metapopulation	-
BNM NiN	0.24	Batch-normalized Maxout Network in Network
ResNet-9	-	CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters
Deep Fried Convnets	0.7	Deep Fried Convnets
EXACT (M3-CNN)	0.33	EXACT: How to Train Your Accuracy
SimpleNetv1	0.25	Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures
NiN	0.5	Network In Network
Tsetlin Machine	1.8	The Tsetlin Machine - A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic
VGG-5 (Spinal FC)	0.28	SpinalNet: Deep Neural Network with Gradual Input
StiDi-BP in R-CSNN	-	Spike time displacement based error backpropagation in convolutional spiking neural networks	-
Explaining and Harnessing Adversarial Examples	0.8	Explaining and Harnessing Adversarial Examples
pFedBreD_ns_mg	-	Personalized Federated Learning with Hidden Information on Personalized Prior	-
Wilson-Cowan model RNN	-	Learning in Wilson-Cowan model for metapopulation	-
RMDL (30 RDLs)	0.18	RMDL: Random Multimodel Deep Learning for Classification
TAAF-CNN	0.48%	Evaluating the Performance of TAAF for image classification models

0 of 80 row(s) selected.