From the model card
Fields below are copied from the tags and counters on the HuggingFace repository facebook/dinov2-base 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)
- 2,843,962
- Likes (HF counter at last fetch)
- 195
- Model card
- https://huggingface.co/facebook/dinov2-base
Use cases
- Extracting general-purpose visual features for downstream tasks
- Zero-shot image retrieval using embedding similarity
- Semantic segmentation backbone fine-tuning
- Few-shot classification with frozen features
Pros
- Self-supervised — no manual labels required for pretraining
- Features transfer well to segmentation, depth, and retrieval
- Apache-2.0 licensed
- Available in small/base/large/giant variants for different resource budgets
Cons
- Requires fine-tuning a head for classification — not plug-and-play for prediction
- Larger ViT variants (large/giant) provide substantially better features
- No text alignment — can't do zero-shot classification like CLIP
- Pretraining data curation pipeline is not fully reproducible
Tags
transformerspytorchsafetensorsdinov2image-feature-extractiondinovisionarxiv:2304.07193license:apache-2.0region:us