Learn DINOv3 - the revolutionary DINO v3 computer vision model by Meta AI. Complete DINOv3 tutorial with PyTorch implementation, interactive DINOv3 demo, DINOv3 OCR applications, and DINOv3 segmentation examples. Self-supervised learning trained on 1.7 billion images with 7B parameters achieving state-of-the-art results.
Breakthrough SSL capabilities that set new standards in computer vision research and AI applications. DINOv3 computer vision model represents a paradigm shift in self-supervised learning for advanced visual understanding and image analysis.
SSL breakthrough: Enables training on 1.7 billion images with 7 billion parameters without human labels. Perfect for annotation-scarce scenarios including satellite imagery, medical imaging, and domain-specific applications where labeled data is expensive or unavailable.
Produces excellent high-resolution features and state-of-the-art performance on dense prediction tasks
Versatile application across vision tasks and domains, all with a frozen backbone (no fine-tuning required)
Includes distilled smaller models (ViT-B, ViT-L) and ConvNeXt variants for deployment flexibility
DINOv3 computer vision model sets new standards in self-supervised learning and vision foundation models for AI applications
We scaled unsupervised training to 7B-parameter models and 1.7B image datasets, using a fraction of compute compared to weakly-supervised methods. Despite keeping backbones frozen during evaluation, they achieve absolute state-of-the-art performance across diverse domains.
High-resolution dense features from a single DINOv3 backbone enable leading performance across vision tasks, including object detection, depth estimation, and segmentation, without any finetuning.
Performance Chart
State-of-the-art results across multiple vision tasksFrom challenging annotation scenarios to efficiency-critical deployments
WRI measures tree canopy heights with DINO, helping civil society organizations worldwide monitor reforestation.
Learn moreNASA JPL uses DINO for Mars exploration robots, enabling multiple vision tasks with minimal compute.
Learn moreOrakl Oncology pre-trains DINO on organoid images, producing a backbone to power prediction of patient responses to cancer treatments.
Learn moreDINOv3 marks a new milestone in self-supervised training at scale
Initial research proof-of-concept, with 80M-parameter models trained on 1M images.
First successful scaling of a SSL algorithm. 1B-parameter models trained on 142M images.
An order of magnitude larger training compared to v2, with particular focus on dense features.
Common questions about DINOv3 computer vision model, implementation, and research
DINOv3 is a state-of-the-art computer vision model trained using self-supervised learning (SSL) on 1.7 billion images. Unlike traditional supervised methods, DINOv3 learns powerful visual representations without requiring human-labeled data, making it highly versatile for various computer vision tasks.
DINOv3 achieves state-of-the-art performance across multiple vision tasks with a frozen backbone, requiring no fine-tuning. It outperforms specialized models on dense prediction tasks like object detection, semantic segmentation, and depth estimation while using 6x larger models and 12x more training data compared to DINOv2.
DINOv3 models are available on Hugging Face Hub, GitHub, and through the official research repository. The models include various sizes (ViT-B, ViT-L) and ConvNeXt variants for different deployment needs. All models are released under a commercial license.
DINOv3 excels in object detection, semantic segmentation, depth estimation, satellite imagery analysis, medical imaging, and any domain where high-quality visual features are needed. Organizations like NASA JPL, World Resources Institute, and Orakl Oncology use DINOv3 for mission-critical applications.
Yes! DINOv3 is released under a commercial-friendly license that allows both research and commercial applications. This makes it ideal for startups, enterprises, and research institutions looking to integrate advanced computer vision capabilities into their products.
Start with our HuggingFace tutorial for step-by-step implementation. For detailed paper analysis, visit our research deep-dive. The models can be loaded with just a few lines of code using HuggingFace transformers library.
DINOv3 models vary in size: ViT-B/16 (94M parameters) runs on consumer GPUs, while ViT-L/16 (304M parameters) requires enterprise-grade hardware. The largest model needs ~1GB VRAM for inference, making it accessible for most modern setups.
DINOv3 specifically targets dense prediction tasks where it outperforms CLIP and offers competitive results to specialized models. Unlike GPT-4V (multimodal), DINOv3 focuses on computer vision foundations, achieving better feature quality for downstream vision tasks. See our detailed comparison.
Meta AI provides both DINOv3 ConvNeXt and Vision Transformer variants. The DINO v3 ConvNeXt models offer CNN-based architecture alternatives with comparable performance to ViT. Download DINOv3 ConvNeXt from HuggingFace or our tutorial page.
Comprehensive DINOv3 benchmarks are available in our DINO v3 paper analysis. The benchmarks include ImageNet classification, COCO object detection, ADE20K segmentation, and NYUv2 depth estimation results. Performance metrics show DINO v3 achieving 87.2% ImageNet accuracy and 58.4 COCO mAP.
The DINO v3 license is commercial-friendly, allowing both research and commercial applications. Meta AI released DINOv3 under a permissive license that enables startups and enterprises to integrate the model into products. Full DINOv3 license details are available in the GitHub repository.
Access research papers, source code, pre-trained models, and documentation
Complete DINOv3 tutorial with PyTorch implementation. Learn DINOv3 HuggingFace integration, ConvNeXt variants, and production deployment
Start DINOv3 TutorialComprehensive DINO v3 paper analysis covering SSL methodology, architecture innovations, and benchmarks. Understanding Meta AI's breakthrough research
Read DINO v3 AnalysisComplete DINOv3 comparison with CLIP, ConvNeXt, and other vision models. DINOv3 benchmarks, performance analysis, and deployment considerations
View DINOv3 BenchmarksFree DINOv3 download from GitHub. Access Meta AI's complete DINO v3 codebase, PyTorch training scripts, and evaluation tools with commercial license
Download DINOv3Ready-to-use DINO v3 HuggingFace models with PyTorch integration. Pre-trained DINOv3 weights for ViT and ConvNeXt architectures
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