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