The facebook/dinov3-vith16plus-pretrain-lvd1689m model is a self-supervised vision transformer available on Hugging Face. It belongs to the DINOv3 family and carries the specific ViT-H/16+ architecture. The model was pre-trained on the LVD-1689M dataset and addresses the need for general-purpose image representations that can be transferred to downstream computer vision tasks without task-specific labeled data.
It performs image feature extraction. The model card lists it under the image-feature-extraction task tag and supplies example code that loads it through the Transformers pipeline for that purpose. Integration with the Hugging Face ecosystem occurs via the Transformers library, where users instantiate an AutoImageProcessor and an AutoModel. The repository provides both the model weights in Safetensors format and the associated code for direct loading or pipeline usage. Notebooks for Google Colab and Kaggle are referenced to support experimentation.
The model originates from AI at Meta. Its paper is identified by the arXiv identifier 2508.10104. The license is explicitly named dinov3-license. The repository itself is hosted on Hugging Face, allowing deployment through standard inference providers or local applications that support the Transformers library.
Dinov3 Vith16plus Pretrain Lvd1689m is an Other AI product. It focuses on learning rich visual representations without labels for downstream computer vision tasks. It is built as an open-source project for developers. Dinov3 Vith16plus Pretrain Lvd1689m is open source under the Open Source license. It runs on the web and API.
Meta builds and maintains Dinov3 Vith16plus Pretrain Lvd1689m, and the product first shipped in 2025. Development happens publicly on GitHub with 11k stars and 4 commits in the last 90 days. Key capabilities include Self-Supervised Learning, Feature Extraction, and Vision Transformer.
Latest indexed changes and source events
facebook/dinov3-vith16plus-pretrain-lvd1689m verified by the PulseGate indexer
Other apps tracked under the same category.
