From the model card
Fields below are copied from the tags and counters on the HuggingFace repository facebook/dinov2-large at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- Pipeline tag
- image-feature-extraction
- Library
- Transformers
- Framework tags
- PyTorch
- Weight formats
- safetensors
- License tag
apache-2.0— read the license file in the repo before relying on it- Papers cited
- arXiv:2304.07193
- Downloads (HF counter at last fetch)
- 768,331
- Likes (HF counter at last fetch)
- 117
- Model card
- https://huggingface.co/facebook/dinov2-large
Use cases
- High-quality feature extraction for dense prediction tasks (segmentation, depth)
- Image retrieval in large-scale datasets
- Backbone for fine-tuning on specialized visual domains
- Benchmarking self-supervised representation quality
Pros
- Substantially outperforms ViT-B on linear probing and transfer tasks
- Self-supervised training — no label dependency during pretraining
- Apache-2.0 licensed
- Available as part of a consistent ViT scale ladder (S/B/L/G)
Cons
- ViT-L requires ~1.3GB weights and ~5GB VRAM for inference
- Slower than ViT-B for latency-sensitive applications
- ViT-G/14 provides further gains if compute allows
- No text alignment — unsuitable for cross-modal tasks
Tags
transformerspytorchsafetensorsdinov2image-feature-extractiondinovisionarxiv:2304.07193license:apache-2.0endpoints_compatibleregion:us