mlsanityData to 7 Oct 2026

Reducing the Amount of Real World Data for Object Detector Training with Synthetic Data

arXiv 2202.00632 ↗First posted 31 Jan 2022cs.CVcited by 4

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Abstract (arXiv)

A number of studies have investigated the training of neural networks with synthetic data for applications in the real world. The aim of this study is to quantify how much real world data can be saved when using a mixed dataset of synthetic and real world data. By modeling the relationship between the number of training examples and detection performance by a simple power law, we find that the need for real world data can be reduced by up to 70% without sacrificing detection performance. The training of object detection networks is especially enhanced by enriching the mixed dataset with classes underrepresented in the real world dataset. The results indicate that mixed datasets with real world data ratios between 5% and 20% reduce the need for real world data the most without reducing the detection performance.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 24 references that are in the map. First posted on arXiv 31 Jan 2022.

Builds on

19 of 24 references.

References · 19

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  3. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  4. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  5. 2018YOLOv3: An Incremental Improvementcited by 2,652
  6. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  7. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  8. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
  9. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,282
  10. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,176
  11. 2017Deep Learning Scaling is Predictable, Empiricallycited by 556
  12. 2018Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomizationcited by 292
7 more
  1. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  2. 2017Playing for Benchmarkscited by 169
  3. 2018Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsingcited by 105
  4. 2020Automated Synthetic-to-Real Generalizationcited by 35
  5. 2021Contrastive Syn-to-Real Generalizationcited by 32
  6. 2019How much real data do we actually need: Analyzing object detection performance using synthetic and real datacited by 29
  7. 2019An "On The Fly" Framework for Efficiently Generating Synthetic Big Data Setscited by 2

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Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Levelcited by 7
  2. 2024A-BDD: Leveraging Data Augmentations for Safe Autonomous Driving in Adverse Weather and Lightingcited by 3
  3. 2025Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategiescited by 2
  4. 2023Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine Learning in Epigraphycited by 1