# TinaFace: Strong but Simple Baseline for Face Detection

arXiv: https://arxiv.org/abs/2011.13183 · HTML: https://mlsanity.com/paper/2011.13183/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 30 of 31)

- [WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild](https://mlsanity.com/paper/2409.12259/) (2024)
- [Towards Large-Scale Small Object Detection: Survey and Benchmarks](https://mlsanity.com/paper/2207.14096/) (2022)
- [Sample and Computation Redistribution for Efficient Face Detection](https://mlsanity.com/paper/2105.04714/) (2021)
- [YOLO5Face: Why Reinventing a Face Detector](https://mlsanity.com/paper/2105.12931/) (2021)
- [KeyPoint Relative Position Encoding for Face Recognition](https://mlsanity.com/paper/2403.14852/) (2024)
- [Disentangle Your Dense Object Detector](https://mlsanity.com/paper/2107.02963/) (2021)
- [MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis](https://mlsanity.com/paper/2107.00396/) (2021)
- [Boosting Entity-aware Image Captioning with Multi-modal Knowledge Graph](https://mlsanity.com/paper/2107.11970/) (2021)
- [FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude](https://mlsanity.com/paper/2306.17206/) (2023)
- [Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition](https://mlsanity.com/paper/2209.14498/) (2022)
- [Going Deeper Into Face Detection: A Survey](https://mlsanity.com/paper/2103.14983/) (2021)
- [Racial Bias within Face Recognition: A Survey](https://mlsanity.com/paper/2305.00817/) (2023)
- [DisguisOR: Holistic Face Anonymization for the Operating Room](https://mlsanity.com/paper/2307.14241/) (2023)
- [Detect Faces Efficiently: A Survey and Evaluations](https://mlsanity.com/paper/2112.01787/) (2021)
- [EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection](https://mlsanity.com/paper/2204.01209/) (2022)
- [Safe Fakes: Evaluating Face Anonymizers for Face Detectors](https://mlsanity.com/paper/2104.11721/) (2021)
- [Are Commercial Face Detection Models as Biased as Academic Models?](https://mlsanity.com/paper/2201.10047/) (2022)
- [EfficientFace: An Efficient Deep Network with Feature Enhancement for Accurate Face Detection](https://mlsanity.com/paper/2302.11816/) (2023)
- [Rethinking the backbone architecture for tiny object detection](https://mlsanity.com/paper/2303.11267/) (2023)
- [The Importance of Anti-Aliasing in Tiny Object Detection](https://mlsanity.com/paper/2310.14221/) (2023)
- [Filter-Pruning of Lightweight Face Detectors Using a Geometric Median Criterion](https://mlsanity.com/paper/2311.16613/) (2023)
- [B-FPGM: Lightweight Face Detection via Bayesian-Optimized Soft FPGM Pruning](https://mlsanity.com/paper/2501.16917/) (2025)
- [Automated Visual Attention Detection using Mobile Eye Tracking in Behavioral Classroom Studies](https://mlsanity.com/paper/2505.07552/) (2025)
- [EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection](https://mlsanity.com/paper/2306.02277/) (2023)
- [Measuring Gender Representation in Animated Films](https://mlsanity.com/paper/2608.21429/) (2026)
- [Bounding-box deep calibration for high performance face detection](https://mlsanity.com/paper/2110.03892/) (2021)
- [Robust Human Identity Anonymization using Pose Estimation](https://mlsanity.com/paper/2301.04243/) (2023)
- [A Lightweight and Accurate Face Detection Algorithm Based on Retinaface](https://mlsanity.com/paper/2308.04340/) (2023)
- [Archival Faces: Detection of Faces in Digitized Historical Documents](https://mlsanity.com/paper/2504.00558/) (2025)
- [Using Cross-Domain Detection Loss to Infer Multi-Scale Information for Improved Tiny Head Tracking](https://mlsanity.com/paper/2505.22677/) (2025)

## Abstract (arXiv)

Face detection has received intensive attention in recent years. Many works present lots of special methods for face detection from different perspectives like model architecture, data augmentation, label assignment and etc., which make the overall algorithm and system become more and more complex. In this paper, we point out that there is no gap between face detection and generic object detection. Then we provide a strong but simple baseline method to deal with face detection named TinaFace. We use ResNet-50 citehe2016deep as backbone, and all modules and techniques in TinaFace are constructed on existing modules, easily implemented and based on generic object detection. On the hard test set of the most popular and challenging face detection benchmark WIDER FACE citeyang2016wider, with single-model and single-scale, our TinaFace achieves 92.1% average precision (AP), which exceeds most of the recent face detectors with larger backbone. And after using test time augmentation (TTA), our TinaFace outperforms the current state-of-the-art method and achieves 92.4% AP. The code will be available at urlhttps://github.com/Media-Smart/vedadet.