Data Card: OneVision Encoder Training Data
February 5, 2026 ยท View on GitHub
Overview
This document describes the datasets used for training OneVision Encoder. The pretraining corpus combines large-scale image and video datasets for unified visual representation learning.
OneVision-Encoder Pretraining Dataset
| Source | Samples | Type | Modality | Temporal | Curation |
|---|---|---|---|---|---|
| LAION-400M | 250M | WebImages | Image | -- | Yes |
| COYO-700M | 400M | WebImages | Image | -- | Yes |
| OBELICS | 15M | Documents | Image | -- | Yes |
| Zero250M | 15M | CuratedImages | Image | -- | Yes |
| ImageNet-21K | 14M | Images | Image | -- | Yes |
| HowTo100M | 50M | ExoVideo | Video | Short | No |
| Panda-70M | 50M | ExoVideo | Video | Long | Yes |
| Kinetics-710 | 658K | ActionVideo | Video | Short | Yes |
| SSV2 | 221K | ActionVideo | Video | Short | Yes |
Dataset Summary
| Category | Total Samples |
|---|---|
| Image | ~694M |
| Video | ~100M+ |
| Total | ~794M+ |
Image Data Annotation
For image data, we primarily process LAION-400M and COYO-700M with the following pipeline:
Deduplication: We employ a Union-Find algorithm to strictly deduplicate the dataset.
Clustering and Multi-label Annotation: We utilize the metaclip-h14-fullcc2.5b model to extract image features and cluster all images into 2 million classes. Based on this clustering, each image sample is annotated with the nearest Top-10 class centers as its multi-label supervision signal.
OCR-based Fine-grained Tagging: Furthermore, we incorporate the OBELICS and Zero250M datasets. We utilize PaddleOCR to recognize text within images and perform word segmentation on the recognized content; the resulting vocabulary is used as multi-labels to construct a supervision signal containing exactly 100 fine-grained tags per image.