# UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

arXiv: https://arxiv.org/abs/2206.12681 · HTML: https://mlsanity.com/paper/2206.12681/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 25 Jun 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges](https://mlsanity.com/paper/2102.12219/) (2021)
- [Towards High-Resolution Salient Object Detection](https://mlsanity.com/paper/1908.07274/) (2019)
- [Learning to Resize Images for Computer Vision Tasks](https://mlsanity.com/paper/2103.09950/) (2021)
- [NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset](https://mlsanity.com/paper/2110.10364/) (2021)
- [High Resolution Medical Image Analysis with Spatial Partitioning](https://mlsanity.com/paper/1909.03108/) (2019)

## Built on by (most cited first, 3 of 3)

- [Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective](https://mlsanity.com/paper/2303.02095/) (2023)
- [Patch Gradient Descent: Training Neural Networks on Very Large Images](https://mlsanity.com/paper/2301.13817/) (2023)
- [Resource Efficient Perception for Vision Systems](https://mlsanity.com/paper/2405.07166/) (2024)

## Abstract (arXiv)

Convolutional neural network (CNN) approaches available in the current literature are designed to work primarily with low-resolution images. When applied on very large images, challenges related to GPU memory, smaller receptive field than needed for semantic correspondence and the need to incorporate multi-scale features arise. The resolution of input images can be reduced, however, with significant loss of critical information. Based on the outlined issues, we introduce a novel research problem of training CNN models for very large images, and present 'UltraMNIST dataset', a simple yet representative benchmark dataset for this task. UltraMNIST has been designed using the popular MNIST digits with additional levels of complexity added to replicate well the challenges of real-world problems. We present two variants of the problem: 'UltraMNIST classification' and 'Budget-aware UltraMNIST classification'. The standard UltraMNIST classification benchmark is intended to facilitate the development of novel CNN training methods that make the effective use of the best available GPU resources. The budget-aware variant is intended to promote development of methods that work under constrained GPU memory. For the development of competitive solutions, we present several baseline models for the standard benchmark and its budget-aware variant. We study the effect of reducing resolution on the performance and present results for baseline models involving pretrained backbones from among the popular state-of-the-art models. Finally, with the presented benchmark dataset and the baselines, we hope to pave the ground for a new generation of CNN methods suitable for handling large images in an efficient and resource-light manner.