# Industrial Synthetic Segment Pre-training

arXiv: https://arxiv.org/abs/2505.13099 · HTML: https://mlsanity.com/paper/2505.13099/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 43 of its 55 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 30 of 43)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Exploring Plain Vision Transformer Backbones for Object Detection](https://mlsanity.com/paper/2203.16527/) (2022)
- [Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding](https://mlsanity.com/paper/2011.02523/) (2020)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [Virtual KITTI 2](https://mlsanity.com/paper/2001.10773/) (2020)
- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [RTMDet: An Empirical Study of Designing Real-Time Object Detectors](https://mlsanity.com/paper/2212.07784/) (2022)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners](https://mlsanity.com/paper/2306.00984/) (2023)
- [Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy](https://mlsanity.com/paper/1912.07726/) (2019)
- [Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications](https://mlsanity.com/paper/2304.05750/) (2023)
- [Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach](https://mlsanity.com/paper/1812.05040/) (2018)
- [Pre-training without Natural Images](https://mlsanity.com/paper/2101.08515/) (2021)
- [The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot](https://mlsanity.com/paper/2306.16623/) (2023)
- [Into the LAIONs Den: Investigating Hate in Multimodal Datasets](https://mlsanity.com/paper/2311.03449/) (2023)

## Abstract (arXiv)

Vision Foundation Models (VFMs) have made remarkable progress and are increasingly being applied to segmentation tasks in real-world industrial settings. However, VFMs pre-trained on real-image datasets still face several challenges: (1) they do not always perform well on industrial datasets due to significant differences from natural imagery, (2) legal and ethical restrictions, such as limitations on commercial use, constrain extensibility, and (3) building training frameworks under limited computational and data resources remains a critical issue. These challenges raise a fundamental question: can we construct industrial segmentation models without relying on real images or manual annotations? To address this question, we propose the Instance Core Segment Dataset (InsCore), a synthetic data generation framework and the resulting pre-training dataset based on Formula-Driven Supervised Learning (FDSL). InsCore is designed not around the visual appearance or domain of real images, but around the hypothesis that learning to handle complex occlusions during pre-training is a key factor for strong performance in industrial domains. Through experiments across five domains (medical, biomedical, remote sensing, manufacturing, and logistics) we demonstrate that InsCore pre-trained models achieve average mAP scores of 45.2 with the ViTDet backbone and 46.0 with the Swin Transformer backbone, on par with ImageNet-21k supervised pre-training (45.0) while using no real images at all. As a reference point under different input assumptions, prompted SAM with ground-truth bounding boxes attains 45.4 on the same benchmarks. Finally, InsCore consists of only 100k images and 3.2M masks, roughly 1/110 and 1/312 the scale of the SA-1B dataset.