# Automatic Photo Orientation Detection with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2305.10319 · HTML: https://mlsanity.com/paper/2305.10319/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 4 of 4)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Understanding Neural Networks Through Deep Visualization](https://mlsanity.com/paper/1506.06579/) (2015)

## Built on by (most cited first, 4 of 4)

- [RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation](https://mlsanity.com/paper/2508.13968/) (2025)
- [Depth-Aware Image and Video Orientation Estimation](https://mlsanity.com/paper/2604.13995/) (2026)
- [How Do ConvNets Understand Image Intensity?](https://mlsanity.com/paper/2306.00360/) (2023)
- [Image Rotation Angle Estimation: Comparing Circular-Aware Methods](https://mlsanity.com/paper/2603.25351/) (2026)

## Abstract (arXiv)

We apply convolutional neural networks (CNN) to the problem of image orientation detection in the context of determining the correct orientation (from 0, 90, 180, and 270 degrees) of a consumer photo. The problem is especially important for digitazing analog photographs. We substantially improve on the published state of the art in terms of the performance on one of the standard datasets, and test our system on a more difficult large dataset of consumer photos. We use Guided Backpropagation to obtain insights into how our CNN detects photo orientation, and to explain its mistakes.