# From Pixels to Titles: Video Game Identification by Screenshots using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2311.15963 · HTML: https://mlsanity.com/paper/2311.15963/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 13 of 13)

- [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)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (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)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [Deep learning for video game genre classification](https://mlsanity.com/paper/2011.12143/) (2020)

## Abstract (arXiv)

This paper investigates video game identification through single screenshots, utilizing ten convolutional neural network (CNN) architectures (VGG16, ResNet50, ResNet152, MobileNet, DenseNet169, DenseNet201, EfficientNetB0, EfficientNetB2, EfficientNetB3, and EfficientNetV2S) and three transformers architectures (ViT-B16, ViT-L32, and SwinT) across 22 home console systems, spanning from Atari 2600 to PlayStation 5, totalling 8,796 games and 170,881 screenshots. Except for VGG16, all CNNs outperformed the transformers in this task. Using ImageNet pre-trained weights as initial weights, EfficientNetV2S achieves the highest average accuracy (77.44%) and the highest accuracy in 16 of the 22 systems. DenseNet201 is the best in four systems and EfficientNetB3 is the best in the remaining two systems. Employing alternative initial weights fine-tuned in an arcade screenshots dataset boosts accuracy for EfficientNet architectures, with the EfficientNetV2S reaching a peak accuracy of 77.63% and demonstrating reduced convergence epochs from 26.9 to 24.5 on average. Overall, the combination of optimal architecture and weights attains 78.79% accuracy, primarily led by EfficientNetV2S in 15 systems. These findings underscore the efficacy of CNNs in video game identification through screenshots.