Heliosoph/dpt-large-onnx

DPT-Large — Monocular Depth Estimation (ONNX)

ONNX export of Intel/dpt-large — the Dense Prediction Transformer for monocular depth. ~330M params, originally published as part of the MiDaS project at Intel Intelligent Systems Lab.

Re-hosted under Heliosoph for distribution stability — Intel's published checkpoint is the authoritative source.

Credit: Intel ISL (DPT / MiDaS team — Ranftl et al.).

What this repo contains

dpt_large_384.onnx     # ~1.3 GB

A single ONNX file. No tokenizer, no preprocessor config — preprocessing is fixed by convention.

Input/output shape

	Spec
Input name	pixel_values (or image — verify in Netron)
Input shape	[1, 3, 384, 384]
Input dtype	float32
Preprocessing	RGB, divide by 255, normalize by mean=[0.5, 0.5, 0.5] / std=[0.5, 0.5, 0.5]
Output shape	[1, 384, 384]
Output meaning	Relative depth — not metric. Lower values = farther; higher values = closer. Linearly map to your visualization range.

How to use

import onnxruntime as ort
import numpy as np
from PIL import Image

sess = ort.InferenceSession("dpt_large_384.onnx")

# Resize input image to 384×384, normalize, NCHW
img = Image.open("photo.jpg").convert("RGB").resize((384, 384))
arr = (np.asarray(img, dtype=np.float32) / 255.0 - 0.5) / 0.5  # HWC, [-1,1]
arr = arr.transpose(2, 0, 1)[None, ...]                         # 1x3x384x384

depth = sess.run(None, {sess.get_inputs()[0].name: arr})[0][0]  # 384x384

For metric depth, pair with a calibration scheme — DPT-Large is trained for relative depth and will not give you "this object is 1.7m away" without further work.

When to pick DPT-Large

• Quality matters more than speed: ~330M params, slowest variant in the MiDaS family.
• Single static image, not video: no temporal smoothing built in.
• GPU available: CPU inference is workable but slow (~1–2 sec on consumer CPU).

For real-time or edge use, prefer dpt-hybrid or midas-small — not in this repo, but available as separate uploads upstream.

License

Apache 2.0 — same as upstream. LICENSE file included.