Towards-Errorless-Training-ImageNet-1k

Towards Errorless Training ImageNet-1k

This repository host MATLAB code and models for the manuscript, Towards Errorless Training ImageNet-1k, which is available at https://arxiv.org/abs/2508.04941. We give 6 models trained on the ImageNet-1k dataset, which we list in the table below. Each model is featured model of archtecture 17x40x2. That is, each model is made up of 17x40x2=1360 FNNs, all with homogeneous architecture (900-256-25 or 900-256-77-25), working in parallel to produce 1360 predictions which determine a final prediction using the majority voting protocol.

We trained the 6 models using the following transformation of the 64x64 downsampled ImageNet-1k dataset:

• downsampled images to 32x32, using the mean values of non-overlapping 2x2 grid cells and
• trimmed off top row, bottom row, left-most column, and right-most column.

This transformed data results in 30x30 images, hence 900-dimensional input vectors.

For a thorough description of our models trained on the ImageNet-1k dataset, please read our preprint linked above.