---
license: mit
---

# DepthART (AX650 Relative Depth Estimation)

DepthART is a family of relative depth estimation models based on selective state
spaces (Selective Scan). This repository provides the ONNX model of the `small / 448`
variant, an `axmodel` compiled for **AX650**, and tools for host ONNX inference, board
axmodel inference, and model rewriting.

The model outputs **affine-invariant relative depth**: larger values mean closer,
smaller values mean farther. It does not directly produce metric depth; align it with
ground truth depth using a global scale/offset before use.

## Single-image result

Left: input image; right: relative depth output by the model (INFERNO colormap,
stretched to the 2%–98% percentile range).

![DepthART single-image inference result](host_depth.jpg)

## Repository contents

| Path | Description |
| --- | --- |
| `models/relative_s_448_default.onnx` | Original FP32 ONNX model, containing the custom op `com.depthart::SelectiveScan` |
| `models/relative_s_448_default_ax650.axmodel` | Compiled AX650 model |
| `src/infer/depthart_infer_onnx.py` | Host ONNX inference (rewrites `SelectiveScan` automatically) |
| `src/infer/depthart_infer_axmodel.py` | AX650 board axmodel inference (standalone script) |
| `src/tool/depthart_export_axonnx.py` | Rewrites `SelectiveScan` into a pure ONNX subgraph that pulsar2 can compile |
| `src/tool/compare_onnx_axmodel.py`, `compare_depth_outputs.py` | Numerical comparison of outputs from different backends |
| `src/tool/evaluate_nyu654_depth.py` | Accuracy evaluation on the NYU Depth V2 eigen test split |
| `host.jpg`, `host_depth.jpg` | Single-image example input and side-by-side visualization result |

## Model interface

### ONNX

| Item | Value |
| --- | --- |
| Input | `image` `[1, 3, 448, 448]` float32, NCHW, RGB |
| Output | `depth` `[1, 1, 448, 448]` float32, relative depth (larger = closer) |

## AX650 board inference

The script `src/infer/depthart_infer_axmodel.py` does not depend on other scripts in
the repository. For deployment, just place the following files in the same directory
on the board:

```text
relative_s_448_default_ax650.axmodel
depthart_infer_axmodel.py
images or image directory to test
```

Board runtime requires `axengine`.

### Single-image validation

```bash
python3 depthart_infer_axmodel.py \
    --axmodel relative_s_448_default_ax650.axmodel \
    --image ./host.jpg \
    --output ./results/ax_single
```

Outputs `results/ax_single/host_depth.jpg` (side-by-side visualization) and
`host_depth.npy`; the console prints depth statistics, NPU inference time, CPU
processing time, and per-stage timings. A single image is the first inference and
includes runtime first-execution overhead; use dataset mode for performance evaluation.

### Dataset inference

By default, fast mode only measures `read / preprocess / npu` and skips depth
restoration, visualization, and file writing, for performance testing:

```bash
python3 depthart_infer_axmodel.py \
    --axmodel relative_s_448_default_ax650.axmodel \
    --dataset ./images \
    --output ./results/board
```

Full output (depth restoration, visualization, summary NPZ), for accuracy evaluation:

```bash
python3 depthart_infer_axmodel.py \
    --axmodel relative_s_448_default_ax650.axmodel \
    --dataset ./images \
    --output ./results/board_full \
    --full-output \
    --dump-npz ./results/board_full/raw_outputs.npz
```

Image-by-image comparison against the host ONNX baseline summary NPZ:

```bash
python3 depthart_infer_axmodel.py \
    --axmodel relative_s_448_default_ax650.axmodel \
    --dataset ./images \
    --output ./results/board_check \
    --ref-npz ./results/onnx_full/raw_outputs.npz
```