---
license: cc-by-nc-sa-4.0
task_categories:
- image-to-image
tags:
- optical-flow
- motion-estimation
pretty_name: DinoFlow
size_categories:
- 100K<n<1M
configs:
- config_name: train
  default: true
  data_files:
  - split: train
    path: train/*.parquet
- config_name: sintel-clean
  data_files:
  - split: test
    path: eval/sintel-clean-*.parquet
- config_name: sintel-final
  data_files:
  - split: test
    path: eval/sintel-final-*.parquet
- config_name: kitti2015
  data_files:
  - split: test
    path: eval/kitti2015-*.parquet
---

![DinoFlow](cover.png)

# DinoFlow

A harmonized, pre-shuffled corpus of **61,514 (frame1, frame2, flow) pairs** for training
two-frame **optical-flow** models on a frozen DINOv3 backbone. The synthetic training sources are
decoded to a **single dense-flow convention**, packed into uniform ~1 GB Parquet shards, and
**globally shuffled** so any shard is a representative sample of the whole.

## Schema

| column | type | description |
|---|---|---|
| `dataset` | string | source dataset tag |
| `height`, `width` | int32 | native resolution of the stored arrays |
| `image1` | Image | frame *t* (PNG, lossless RGB) |
| `image2` | Image | frame *t+1* (PNG, lossless RGB) |
| `flow` | binary | float16 LE, `(H, W, 2)` row-major, `(u, v)` pixels (frame1 &rarr; frame2) |
| `valid` | Image | uint8 1-channel PNG, `255` = valid, `0` = invalid/occluded |

```python
import io, numpy as np
from PIL import Image
from datasets import load_dataset

ds = load_dataset("blanchon/dinoflow-dataset", split="train", streaming=True)
row = next(iter(ds))
frame1 = row["image1"]                                              # PIL RGB
frame2 = row["image2"]                                              # PIL RGB
H, W = row["height"], row["width"]
flow = np.frombuffer(row["flow"], "<f2").reshape(H, W, 2)           # (u, v) pixels
valid = np.asarray(row["valid"]) > 0                               # bool (H, W)
```

## Composition

| source | pairs | domain | flow |
|---|---:|---|---|
| FlyingThings3D_subset | 39,282 | objects in 3D scenes (synthetic) | dense |
| FlyingChairs | 22,232 | chairs over backgrounds (synthetic) | dense |
| **total** | **61,514** | | |

## Harmonization

Every source is decoded to **dense flow `(u, v)` in pixels** (frame1 &rarr; frame2), stored as
compact little-endian **float16** (near-lossless for magnitudes &lt; ~1000 px). The synthetic flow
is dense; pixels with `|u|` or `|v|` &ge; 1000 px are marked invalid in `valid` (the FlowNet/RAFT
convention). Frames are stored losslessly as PNG at native resolution; resize/crop happens in the
training loader.

## Splits

- **`train`** &mdash; the full 61,514-pair corpus, globally shuffled into 121 shards (~1 GB
  each, small row groups + page index for fast random access).

> The `train` split is the full **FlowNet/RAFT C&rarr;T** training corpus: **FlyingChairs** (the *C*
> stage) and **FlyingThings3D_subset** (the *T* stage), decoded to one flow convention and globally
> shuffled together, so any shard mixes both sources.

## Evaluation (held-out)

Standard optical-flow benchmarks, kept **out of training** and shipped as separate configs. Report
**EPE** (mean end-point error over valid pixels) and, on KITTI, **Fl-all** (outlier rate):

| config | benchmark | notes |
|---|---|---|
| `sintel-clean` | MPI-Sintel (clean) | movie-grade synthetic; dense flow; clean render pass |
| `sintel-final` | MPI-Sintel (final) | as clean, with motion blur + atmospherics |
| `kitti2015` | KITTI-2015 flow | driving; 200 pairs; sparse LiDAR-derived flow + valid mask |

```python
ev = load_dataset("blanchon/dinoflow-dataset", name="sintel-clean", split="test")
```

## License

Released for **non-commercial research** under **CC BY-NC-SA 4.0**. Each source retains its original
license &mdash; respect the original terms of each source (FlyingChairs / FlyingThings3D per their
upstream LMB terms; MPI-Sintel and KITTI-2015 per their respective terms).

---

<sub>Recipe mirrors <b>DinoDepth</b> / <b>AnyDepth</b> (arXiv:2601.02760), for optical flow.</sub>