MiDaS v2.1 Small — Monocular Depth Estimation (ONNX)

ONNX checkpoint of Intel ISL's MiDaS v2.1 small — an EfficientNet-Lite3 encoder paired with a lightweight depth decoder. ~21M params, 256×256 input, CPU-friendly. Sibling to DPT-Large but ~16× smaller and ~20× faster on CPU.

Not converted locally — this is the ONNX file isl-org publishes directly in the v2_1 GitHub release.

Credit: Intel Intelligent Systems Lab (MiDaS team — Ranftl, Lasinger, Hafner, Schindler, Koltun).

What this repo contains

midas_v21_small_256.onnx   # ~80 MB — fp32, EfficientNet-Lite3 backbone, 256×256 input

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

Input / output

	Spec
Input name	`input.1` (verify in Netron)
Input shape	`[1, 3, 256, 256]` (NCHW)
Input dtype	float32
Input color order	BGR — note this differs from DPT-Large (which expects RGB)
Preprocessing	Resize to 256×256, scale to `[0,1]`, normalize with ImageNet stats: `mean=[0.485, 0.456, 0.406]`, `std=[0.229, 0.224, 0.225]`
Output shape	`[1, 256, 256]`
Output meaning	Single-channel relative depth (higher = closer, lower = farther). Not metric. Linearly map to your visualization range.

How to use

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

sess = ort.InferenceSession("midas_v21_small_256.onnx")

# Resize, BGR (note: PIL is RGB by default — swap channels for MiDaS-small)
img = Image.open("photo.jpg").convert("RGB").resize((256, 256))
arr = np.asarray(img, dtype=np.float32) / 255.0
arr = arr[..., ::-1]                                                   # RGB -> BGR
arr = (arr - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]            # ImageNet normalize
arr = arr.transpose(2, 0, 1)[None, ...].copy().astype(np.float32)      # NCHW

depth = sess.run(None, {sess.get_inputs()[0].name: arr})[0][0]         # 256x256

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

When to pick MiDaS-small

Real-time, edge, CPU, or mobile: ~50 ms / image on consumer CPU, ~80 MB on disk.
Coarse depth is enough: relative ordering of "what's close vs far" matters more than fine boundary precision.
Pair with DPT-Large: a common pattern is to run MiDaS-small first for a quick estimate, then fall back to DPT-Large only when high-quality depth is needed for a specific frame.

For sharper boundaries and higher absolute quality (at ~16× the disk + GPU latency), reach for dpt-large instead — same model family, same upstream lab.

License

MIT — same as the upstream isl-org/MiDaS repo. LICENSE file included.