Image Classification on MNIST

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Percentage error 낮을수록 좋음

Rank	Model	Percentage error	Accuracy	Trainable Parameters	Cross Entropy Loss	Epochs	Top 1 Accuracy	Paper	Code	Year
1	Branching/Merging CNN + Homogeneous Vector Capsules	0.13	99.87	1514187	–	–	–	No Routing Needed Between Capsules	AdamByerly/BMCNNwHFCs	2020
2	EnsNet (Ensemble learning in CNN augmented with fully connected subnetworks)	0.16	99.84	–	–	–	–	Ensemble learning in CNN augmented with fully connected subnetworks	eaguaida/TF_EnsNet- · dslisleedh/EnsNet-tensorflow2	2020
2	Efficient-CapsNet	0.16	99.84	161824	–	–	–	Efficient-CapsNet: Capsule Network with Self-Attention Routing	EscVM/Efficient-CapsNet · kaparoo/Efficient-CapsNet	2021
4	SOPCNN (Only a single Model)	0.17	99.83	1400000	–	–	–	Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods	junaidaliop/MNIST-SOPCNN	2020
5	RMDL (30 RDLs)	0.18	99.82	–	–	–	–	RMDL: Random Multimodel Deep Learning for Classification	kk7nc/RMDL	2018
6	R-ExplaiNet-22 (single model)	0.20	99.80	743882	–	–	–	Learning local discrete features in explainable-by-design convolutional neural networks	pikaplan/LearnExplaiNet	2024
7	DropConnect	0.21	99.77	–	–	–	–	Regularization of Neural Networks using DropConnect	mlpack/mlpack	2013
8	MCDNN	0.23	–	–	–	–	–	Multi-column Deep Neural Networks for Image Classification	hughperkins/DeepCL	2012
8	APAC	0.23	–	–	–	–	–	APAC: Augmented PAttern Classification with Neural Networks		2015
10	BNM NiN	0.24	–	–	–	–	–	Batch-normalized Maxout Network in Network	JohnBensen1000/machine_learning	2015
11	SimpleNetv1	0.25	–	–	–	–	–	Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures	Coderx7/SimpleNet · Coderx7/SimpleNet_Pytorch · JavierAntoran/moby_dick_whale_audio_detection · +6	2016
11	CapsNet	0.25	–	–	–	–	–	Dynamic Routing Between Capsules	labmlai/annotated_deep_learning_paper_implementations · naturomics/CapsNet-Tensorflow · Sarasra/models · +74	2017
13	VGG8B + LocalLearning + CO	0.26	–	–	–	–	–	Training Neural Networks with Local Error Signals	anokland/local-loss · ai-tech-research-lab/nitro-d	2019
14	VGG-5 (Spinal FC)	0.28	99.72	–	–	–	–	SpinalNet: Deep Neural Network with Gradual Input	dipuk0506/SpinalNet · dipuk0506/uq · Mechachleopteryx/SpinalNet	2020
15	TextCaps	0.29	99.71	–	–	–	–	TextCaps : Handwritten Character Recognition with Very Small Datasets	vinojjayasundara/textcaps · milanzongor/unihack_2020 · kubantjan/fast-form	2019
15	ExquisiteNetV2	0.29	99.71	518230	–	–	–	A Novel lightweight Convolutional Neural Network, ExquisiteNetV2	shyhyawJou/ExquisiteNetV2	2021
15	WaveMix-128/7	0.29	–	–	–	–	–	WaveMix: Resource-efficient Token Mixing for Images	pranavphoenix/WaveMix	2022
18	Fractional MP	0.3	–	–	–	–	–	Fractional Max-Pooling	facebookresearch/SparseConvNet · laplacetw/vgg-like-cifar10 · VladimirGol/KerasFractionalMaxPooling · +2	2014
18	Tree+Max-Avg pooling	0.3	–	–	–	–	–	Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree	cypw/DPNs · BeanGreen247/Python-AI-Arts	2015
18	CMsC	0.3	–	–	–	–	–	Competitive Multi-scale Convolution		2015
21	EXACT (M3-CNN)	0.33	–	–	–	–	–	EXACT: How to Train Your Accuracy	tinkoff-ai/exact · ivan-chai/exact	2022
22	Second Order Neural Ordinary Differential Equation	0.37	99.63	–	–	–	–	On Second Order Behaviour in Augmented Neural ODEs	a-norcliffe/sonode	2020
22	Augmented Neural Ordinary Differential Equation	0.37	99.63	–	–	–	–	Augmented Neural ODEs	EmilienDupont/augmented-neural-odes · mitmath/18S096SciML · locuslab/monotone_op_net · +3	2019
24	DSN	0.4	–	–	–	–	–	Deeply-Supervised Nets	ellisdg/3DUnetCNN	2014
24	CKN	0.4	–	–	–	–	–	Convolutional Kernel Networks		2014
24	C-SVDDNet	0.4	–	–	–	–	–	Unsupervised Feature Learning with C-SVDDNet		2014
24	HOPE	0.4	–	–	–	–	–	Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks		2015
24	FLSCNN	0.4	–	–	–	–	–	Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network		2015
24	MIM	0.4	–	–	–	–	–	On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units		2015
24	Fitnet-LSUV-SVM	0.4	–	–	–	–	–	All you need is a good init	ducha-aiki/LSUVinit · ducha-aiki/LSUV-keras · ducha-aiki/LSUV-pytorch · +8	2015
31	TAAF-CNN	0.48%	99.52%	421642	0.0188	35	–	Evaluating the Performance of TAAF for image classification models	bryn-gnolbs/TAAF-for-Image-Classification	2025
32	Neural Architecture Search (NAS)-enabled Convolutional Neural Network (CNN)	0.5	99.5	1882602	–	–	–
32	Maxout Networks	0.5	–	–	–	–	–	Maxout Networks	MaximeVandegar/Papers-in-100-Lines-of-Code · philipperemy/tensorflow-maxout · mavenlin/cuda-convnet · +4	2013
32	NiN	0.5	–	–	–	–	–	Network In Network	MaximeVandegar/Papers-in-100-Lines-of-Code · nagadomi/kaggle-cifar10-torch7 · yuxiangalvin/DeepLOB-Model-Implementation-Project · +14	2013
32	ReNet	0.5	–	–	–	–	–	ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks	fvisin/reseg · brian-moser/dartsrenet · NisTa24/ReNet-Implementation · +1	2015
32	DCNN+GFE	0.5	–	–	–	–	–	Deep Convolutional Neural Networks as Generic Feature Extractors		2017
32	VDN	0.5	–	–	–	–	–	Training Very Deep Networks	LiyuanLucasLiu/LM-LSTM-CRF · yoonkim/lstm-char-cnn · flukeskywalker/highway-networks	2015
38	NeuPDE	0.51	–	–	–	–	–	NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data		2019
39	Simple CNN with BaikalCMA loss	0.53	–	–	–	–	–	Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization	sgonzalez/SwiftGenetics · sgonzalez/SwiftCMA	2019
40	SEER (RegNet10B)	0.58	99.42	–	–	–	–	Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision	facebookresearch/vissl	2022
41	Convolutional Tsetlin Machine	0.6	99.4	–	–	–	–	The Convolutional Tsetlin Machine	cair/TsetlinMachine · cair/pyTsetlinMachine · cair/convolutional-tsetlin-machine-tutorial · +6	2019
41	PCANet	0.6	–	–	–	–	–	PCANet: A Simple Deep Learning Baseline for Image Classification?	Ldpe2G/PCANet · lucasleesw/PCANet_python_ver	2014
41	DiffPrune (LeNet5)	0.6	–	–	–	–	–	DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and $L_0$ Regularization	YanivShu/diffprune_public	2020
44	Deep Fried Convnets	0.7	–	–	–	–	–	Deep Fried Convnets	v0lta/wavelet-network-compression	2014
45	Sparse Activity and Sparse Connectivity in Supervised Learning	0.8	–	–	–	–	–	Sparse Activity and Sparse Connectivity in Supervised Learning		2016
45	Explaining and Harnessing Adversarial Examples	0.8	–	–	–	–	–	Explaining and Harnessing Adversarial Examples	cleverhans-lab/cleverhans · openai/cleverhans · tensorflow/cleverhans · +56	2014
47	BinaryConnect	1.0	–	–	–	–	–	BinaryConnect: Training Deep Neural Networks with binary weights during propagations	tensorpack/tensorpack · MatthieuCourbariaux/BinaryConnect · ryuz/BinaryBrain · +2	2015
48	Convolutional PMM (Parametric Matrix Model)	1.01	98.99	129416	–	–	–	Parametric Matrix Models		2024
49	LeNet 300-100 (Sparse Momentum)	1.26	–	–	–	–	–	Sparse Networks from Scratch: Faster Training without Losing Performance	TimDettmers/sparse_learning · google-research/rigl	2019
50	Convolutional Clustering	1.4	–	–	–	–	–	Convolutional Clustering for Unsupervised Learning		2015