Dpt Large 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.
Source du modèle
- Type
- Other
- Taille du fichier
- 1,27 GB
- Modèle de base
- Intel/dpt-large
- Version
- vDpt large 384
- Mis à jour
- 10 oct. 2026
- Source
- Hugging Face
Description de la source
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 Intel's published checkpoint on HuggingFace. LICENSE file included.
Note: the original isl-org/MiDaS GitHub repo (where the DPT architecture was first released) is MIT. Intel re-released the trained DPT-Large weights on HuggingFace under Apache-2.0, which is what this repo mirrors. Same model family, different distribution channel, different licenses. The midas-small Heliosoph repo (sourced from the GitHub release) inherits MIT.
Sources
1 source
Sources
1 sourceVérifié 10 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
2 extraits
Extraits de sources
2 extraits--- license: apache-2.0 library_name: onnx tags: - depth-estimation - dpt - midas - onnx base_model: Intel/dpt-large pipeline_tag: depth-estimation --- # DPT-Large — Monocular Depth Estimation (ONNX) ONNX export of [Intel/dpt-large](https://huggingface.co/Intel/dpt-large) — the Dense Prediction Transformer for monocular depth. ~330M params, originally published as part of the [MiDaS](https://github.com/isl-org/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 ```python 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 [Intel's published checkpoint on HuggingFace](https://huggingface.co/Intel/d...
Source context: 0 downloads · 0 likes · Pipeline depth-estimation · Library onnx · Repo Heliosoph/dpt-large-onnx
