Model overview
A DINOv3 ViT model image feature encoder. Distilled on LVD-1689M from the DINOv3 ViT-7B model. * The original model weights ended up with all QKV projection biases being zeroes. For `timm`, have disabled the QKV bias (`qkv_bias=False`) for the models and not loaded the zero weights. For some model sizes there are variants with `qkvb` in the name that have the bias enabled (`qkv_bias=True`), but zero, to match the behaviour of `transformers` and original models. * The original models keep RoPE periods as a persistent `bfloat16` buffer. `timm` generates `float32` periods at init. This results in some numerical differences, however the `timm` approach should be less problematic running on devices without bfloat16 support, and appears to work as well if not slightly better for fine-tuning. `model.rope.periods = model.rope.periods.to(torch.bfloat16).to(torch.float32)` will truncate the periods to bfloat16 and result in matching outputs. - **Model Type:** Image Feature Encoder - **Model Stats:** - Params (M): 85.6 - GMACs: 23.6 - Activations (M): 34.1 - Image size: 256 x 256 - **Original:** https://github.com/facebookresearch/dinov3 - **License:** DINOv3 - **Dataset:** LVD-1689M - **Papers:** - DINOv3: https://arxiv.org/abs/2508.10104 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Public benchmarks & run configuration
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Sources & traceability
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