# Toward Errorless Training ImageNet-1k

Source: [Toward Errorless Training ImageNet-1k](<https://www.sota2.com/research/paper/68fa7ad31e209131cea85540>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:27:47.170Z

Authors: Bo Deng, Levi Heath

Published: 2025-08-06T23:58:56.000Z

## Abstract

In this paper, we describe a feedforward artificial neural network trained on the ImageNet 2012 contest dataset \[7\] with the new method of \[5\] to an accuracy rate of 98.3% with a 99.69 Top-1 rate, and an average of 285.9 labels that are perfectly classified over the 10 batch partitions of the dataset. The best performing model uses 322,430,160 parameters, with 4 decimal places precision. We conjecture that the reason our model does not achieve a 100% accuracy rate is due to a double-labeling problem, by which there are duplicate images in the dataset with different labels.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2508.04941>)

[PDF](<https://arxiv.org/pdf/2508.04941.pdf>)