# Photozilla: A Large-Scale Photography Dataset and Visual Embedding for 20 Photography Styles

arXiv: https://arxiv.org/abs/2106.11359 · HTML: https://mlsanity.com/paper/2106.11359/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Recognizing Image Style](https://mlsanity.com/paper/1311.3715/) (2013)
- [A Survey on Food Computing](https://mlsanity.com/paper/1808.07202/) (2018)
- [BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography](https://mlsanity.com/paper/1704.08614/) (2017)
- [FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging](https://mlsanity.com/paper/1909.11946/) (2019)
- [The iMet Collection 2019 Challenge Dataset](https://mlsanity.com/paper/1906.00901/) (2019)
- [EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets](https://mlsanity.com/paper/2006.08369/) (2020)
- [Urban Crowdsensing using Social Media: An Empirical Study on Transformer and Recurrent Neural Networks](https://mlsanity.com/paper/2012.03057/) (2020)

## Built on by (most cited first, 2 of 2)

- [Optimizing Group Utility in Itinerary Planning: A Strategic and Crowd-Aware Approach](https://mlsanity.com/paper/2304.08495/) (2023)
- [Redefining Generalization in Visual Domains: A Two-Axis Framework for Fake Image Detection with FusionDetect](https://mlsanity.com/paper/2510.05740/) (2025)

## Abstract (arXiv)

The advent of social media platforms has been a catalyst for the development of digital photography that engendered a boom in vision applications. With this motivation, we introduce a large-scale dataset termed 'Photozilla', which includes over 990k images belonging to 10 different photographic styles. The dataset is then used to train 3 classification models to automatically classify the images into the relevant style which resulted in an accuracy of ~96%. With the rapid evolution of digital photography, we have seen new types of photography styles emerging at an exponential rate. On that account, we present a novel Siamese-based network that uses the trained classification models as the base architecture to adapt and classify unseen styles with only 25 training samples. We report an accuracy of over 68% for identifying 10 other distinct types of photography styles. This dataset can be found at https://trisha025.github.io/Photozilla/