# View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network

arXiv: https://arxiv.org/abs/1702.01721 · HTML: https://mlsanity.com/paper/1702.01721/

## Key facts

- Cited by 9 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 6 Feb 2017.

## Built on by (most cited first, 9 of 9)

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- [All You Need to Know About Training Image Retrieval Models](https://mlsanity.com/paper/2503.13045/) (2025)
- [Real-time smart vehicle surveillance system](https://mlsanity.com/paper/2111.12289/) (2021)
- [Adaptive Prompt Tuning: Vision Guided Prompt Tuning with Cross-Attention for Fine-Grained Few-Shot Learning](https://mlsanity.com/paper/2412.14640/) (2024)
- [Graph Neural Networks for Semi-Supervised Image Classification with Multi-Feature Aggregation](https://mlsanity.com/paper/2606.17406/) (2026)

## Abstract (arXiv)

This paper describes the details of Sighthound's fully automated vehicle make, model and color recognition system. The backbone of our system is a deep convolutional neural network that is not only computationally inexpensive, but also provides state-of-the-art results on several competitive benchmarks. Additionally, our deep network is trained on a large dataset of several million images which are labeled through a semi-automated process. Finally we test our system on several public datasets as well as our own internal test dataset. Our results show that we outperform other methods on all benchmarks by significant margins. Our model is available to developers through the Sighthound Cloud API at https://www.sighthound.com/products/cloud