---
license: other
license_name: dinov3-license
license_link: LICENSE.md
base_model: facebook/dinov3-vits16-pretrain-lvd1689m
tags:
  - dinov3
  - onnx
  - tensorrt
  - jetson
  - image-feature-extraction
library_name: vision-rt
pipeline_tag: image-feature-extraction
---

# kornia/dinov3

ONNX export of **DINOv3 ViT-S/16** for [`vision-rt`](https://github.com/kornia/vision-rt),
producing a global image descriptor (the CLS token) for retrieval, visual place
recognition and scene-change detection on NVIDIA Jetson.

**All model credit belongs to Meta AI** (Siméoni et al.). This repository contains only a
format conversion of
[`facebook/dinov3-vits16-pretrain-lvd1689m`](https://huggingface.co/facebook/dinov3-vits16-pretrain-lvd1689m)
— no retraining, no fine-tuning, no architectural change. Use of these files is governed
by the **DINOv3 License**, reproduced in full in [`LICENSE.md`](LICENSE.md); by
downloading them you agree to its terms, including its restrictions on military,
weapons-related and other prohibited uses.

## Files

| File | Notes |
|------|-------|
| `dinov3-vits16-336.onnx` | Graph only, ~1.2 MB |
| `dinov3-vits16-336.onnx.data` | **Required** external weights, ~86 MB |

The `.onnx.data` sidecar is mandatory — torch's exporter externalizes weights regardless
of the 2 GB protobuf limit, so the `.onnx` alone contains no weights. Keep the two files
in the same directory; the ONNX parser resolves the sidecar next to the graph.

## Model I/O

Input `input` `[1, 3, 336, 336]`, ImageNet-normalized CHW (mean `[0.485, 0.456, 0.406]`,
std `[0.229, 0.224, 0.225]`), 336 = 21 × 16.

| Output | Shape | Meaning |
|--------|-------|---------|
| `descriptor` | `[1, 384]` | CLS token (`pooler_output`) — the global descriptor |
| `tokens` | `[1, 446, 384]` | Full sequence: 1 CLS + 4 registers + 441 patches (21×21) |

Verified at export: `cos(pooler_output, last_hidden_state[:,0]) = 1.000000`, so the
descriptor *is* the CLS token. Note the 4 **register tokens** between CLS and the patch
grid — any hand-rolled pooling must skip indices 1–4 or it averages in artifact tokens.

Exported with opset 18 from torch 2.11 / transformers 4.57.6 by
[`crates/vrt-dinov3/scripts/export_dinov3.py`](https://github.com/kornia/vision-rt).

## ⚠️ Build BF16, not FP16

**FP16 produces all-NaN for this model.** Measured on Jetson Orin (SM87, TensorRT
10.3.0.30), descriptor cosine against the PyTorch reference:

| Precision | GPU compute | Cosine vs PyTorch | |
|-----------|------------:|------------------:|--|
| **bf16** | **7.6 ms** | **0.999568** | ✅ recommended |
| fp32 | 13.4 ms | 0.999998 | ✅ |
| fp16 | 5.2 ms | all NaN | ❌ |
| fp16 + bf16 | 5.2 ms | all NaN | ❌ TRT picks fp16 for attention |

Cause: exactly one tensor leaves fp16 range — the attention logits `Q·Kᵀ`, max |value|
**2.1e6** (2.5e6 on a natural image) against fp16's 65,504 ceiling. They overflow to `inf`
in the first block, `softmax(inf − inf)` is NaN, and it reaches every output. Every other
tensor stays under 1800. These are already-scaled logits — DINOv3 genuinely produces
massive attention values (the "attention sink" its register tokens absorb). It is a
dynamic-**range** problem, so bf16 fixes it by keeping fp32's exponent.

**Do not try to pin fp16 with `--layerPrecisions`.** TensorRT fuses the attention block
into a myelin kernel, so ONNX node names are absent from the engine; both wildcard and
explicit pins match nothing and are silently ignored — the build succeeds, runs at
identical speed, and still emits NaN.

```bash
trtexec --onnx=dinov3-vits16-336.onnx --saveEngine=dinov3.engine --bf16 \
        --memPoolSize=workspace:2048
```

## Usage

```rust
let dino = DinoV3::from_hub(stream.clone())?;   // or from_engine_file / from_onnx
let mut r = dino.alloc_result()?;
dino.submit(&img, &mut r)?;                      // enqueue, no sync
stream.synchronize()?;
let d = r.descriptor_host()?;                    // L2-normed [384], cosine-ready
```

Measured separation (real images): two views of one place score **~0.96**; unrelated
scenes **−0.001 … 0.11**. Live on a 1280×720 RTSP camera: 15 fps, source-gated, with the
GPU using 6.8 ms of the 66 ms frame interval.

## Citation

```bibtex
@article{simeoni2025dinov3,
  title={DINOv3},
  author={Sim{\'e}oni, Oriane and others},
  journal={arXiv preprint arXiv:2508.10104},
  year={2025}
}
```