---
license: other
library_name: bagel-mot
language:
- en
tags:
- geoug
- geometry
- depth-estimation
---

# GeoUG-Depth

[Project page](https://yangr116.github.io/geoug-project-page/) ·
[Paper](https://yangr116.github.io/geoug-project-page/assets/geoug-paper.pdf)

**Metric depth from a single RGB image.**

GeoUG-Depth is the standalone metric-depth generation expert released with
GeoUG. It generates depth in meters for geometric measurement and downstream
3D reasoning. It is a separate checkpoint from the adaptive spatial RL policy.

| Property | Value |
|---|---|
| Task | Monocular metric-depth generation |
| Output | Depth array in meters and a visualization |
| Weights | EMA metric-depth expert |
| Precision | BF16 release converted from the FP32 source EMA |
| Normalization | Multiscale disparity, `c: [2.0, 8.0, 32.0]` |

## Download

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="rayruiyang/GeoUG-Depth",
    local_dir="ckpt/geoug-metric-depth",
)
```

These are custom BAGEL/MoT weights. Use the GeoUG runtime rather than a generic
`AutoModel.from_pretrained` call. The weight repository contains the checkpoint
assets; inference and training code live in the GeoUG code checkout.

Verify the download:

```bash
(cd ckpt/geoug-metric-depth && sha256sum -c checksums.sha256)
```

## Quick start

Follow the GeoUG checkout's standalone inference installation instructions
(Python 3.10/3.11, CUDA, and the documented FlashAttention environment). From
the checkout root, after downloading the package:

```bash
python -m inference.depth \
  --model-path ckpt/geoug-metric-depth \
  --image /path/to/image.jpg \
  --num-timesteps 50 \
  --cfg-text-scale 4.0 \
  --cfg-img-scale 1.5 \
  --output-dir outputs/inference/depth
```

The loader selects the package's `transform_config.yaml` and EMA weights by
default. The default prompt is:

```text
<GENERATE_DEPTH>Estimate the metric depth map.</GENERATE_DEPTH>
```

Use the `.npy` output for metric depth. The PNG output is a percentile-normalized
visualization and does not encode metric depth values. The source FP32 weights
were preserved; this repository distributes the BF16 inference copy.

## Reported results and evaluation

The project reports the following **affine-aligned** results:

| Dataset | AbsRel (%) ↓ | δ1 (%) ↑ |
|---|---:|---:|
| NYU-v2 | 3.32 | 98.46 |
| KITTI | 4.84 | 97.27 |
| ETH3D | 3.75 | 97.85 |

These aligned metrics should not be interpreted as unaligned metric-scale
accuracy. Keep alignment, image preprocessing, and benchmark configuration
fixed when comparing results.

For evaluation, obtain the benchmark data separately, configure local dataset
paths, and run from the GeoUG checkout:

```bash
python -m evaluation.depth.eval_depth \
  --model-path ckpt/geoug-metric-depth \
  --dataset-config /path/to/depth_datasets.json \
  --depth-type metric
```

See `evaluation/depth/README.md` for dataset configuration and scoring protocols.
Generated depth is an estimate; geometry recovered from it also depends on
camera calibration and the scene distribution.

## Model family

| Checkpoint | Intended use |
|---|---|
| [GeoUG-Spatial](https://huggingface.co/rayruiyang/GeoUG-Spatial) | Final adaptive spatial reasoning policy |
| [GeoUG-Depth](https://huggingface.co/rayruiyang/GeoUG-Depth) | Standalone metric-depth generation |
| [GeoUG-ColdStart](https://huggingface.co/rayruiyang/GeoUG-ColdStart) | SFT initialization for spatial-policy RL |

## Package and integrity

| File | Contents |
|---|---|
| `ema.safetensors` | BF16 EMA model weights, approximately 27.21 GiB |
| `ae.safetensors` | VAE weights |
| `llm_config.json`, `vit_config.json`, `config.json`, `generation_config.json` | Model and generation configuration |
| `tokenizer.json`, `tokenizer_config.json`, `vocab.json`, `merges.txt` | Tokenizer assets |
| `transform_config.yaml` | Image/depth preprocessing and normalization |
| `checksums.sha256` | SHA-256 checksums for the package files |

Download the complete repository. The release uses EMA weights and does not
require a separate non-EMA checkpoint or a shard index.

## Checkpoint provenance

The release is the metric-depth expert EMA checkpoint at training step 6000.
The published weights are the BF16 conversion of the original FP32 EMA.

## License and attribution

GeoUG builds on the BAGEL/MoT architecture and its pretrained components.
This checkpoint accompanies the mixed-license GeoUG code release. Consult
`LICENSE` and `THIRD_PARTY_NOTICES.md` in the GeoUG code checkout for the
applicable component terms, including the CC BY-NC restrictions on the listed
BAGEL/DiT-derived code files. This card does not assign a new blanket license
to upstream components. The data repositories document their own source and
usage terms separately.

## Contact

For questions about this release, contact
[rayyang0116@connect.hku.hk](mailto:rayyang0116@connect.hku.hk).