README.md

metadata

license: mit
library_name: litert
pipeline_tag: depth-estimation
tags:
  - litert
  - tflite
  - depth-estimation
  - midas
  - on-device
  - android
  - gpu

MiDaS small — LiteRT (fp16, NHWC, GPU-clean)

midas_small_256_fp16.tflite is MiDaS v2.1 small (MiDaS_small, the CNN MiDaS with an EfficientNet-Lite3 backbone — not the DPT/ViT variants) converted to LiteRT for on-device monocular depth estimation. Given one RGB image it predicts a per-pixel inverse-depth map (near = bright, far = dark).

It is the model used by the LiteRT compiled_model_api/depth_estimation Android sample.

Files

File	Precision	Size
midas_small_256_fp16.tflite	fp16 weights	~33 MB

Specs

Task	Monocular depth estimation
Source	torch.hub.load("intel-isl/MiDaS", "MiDaS_small")
Input	1 x 256 x 256 x 3 float32, RGB, ImageNet-normalized, NHWC (interleaved)
Output	1 x 256 x 256 float32, relative inverse depth

Pre-processing: resize to 256×256, normalize with ImageNet stats (mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225] on [0,1] pixels), write as interleaved NHWC RGB float32.

Post-processing: min-max normalize the output and map through a color LUT (the sample uses inferno).

Why this conversion

The graph lowers entirely to GPU-clean builtins — no attention, no Flex/Custom ops, no GATHER_ND, no >4D reshapes:

CONV_2D x73, ADD x27, DEPTHWISE_CONV_2D x24, RELU x7, RESIZE_BILINEAR x5, RESHAPE x1

• Channel-last I/O (to_channel_last_io) so the model takes NHWC 1x256x256x3 directly, matching the interleaved RGB the app writes (no input transpose).
• fp16 via AI Edge Quantizer FLOAT_CASTING — half the size, runs natively on the GPU delegate. Dynamic-range int8 is intentionally avoided (it favors the CPU/XNNPACK path, not the GPU delegate).

Fidelity

• Converted fp32 vs. original PyTorch (real image): corr 1.0000, max|diff| ~1.6e-3.
• fp16 vs. fp32: corr 0.9999998 (≈0.27 % of the depth range).

On-device (Pixel 8a, verified)

The fp16 model compiles to 234 / 234 nodes on the LiteRT GPU delegate (LITERT_CL) — full GPU residency, no CPU fallback — at ~1–3 ms / inference (best 1.1 ms). RESIZE_BILINEAR align_corners=True is GPU-supported as-is; no model change needed.

Minimal usage

Android (Kotlin, CompiledModel GPU)

val model = CompiledModel.create(context.assets, "midas_small_256_fp16.tflite",
    CompiledModel.Options(Accelerator.GPU), null)
val inputs = model.createInputBuffers()
val outputs = model.createOutputBuffers()
inputs[0].writeFloat(nhwc)             // [1,256,256,3] ImageNet-normalized RGB, NHWC
model.run(inputs, outputs)
val depth = outputs[0].readFloat()     // [1,256,256] relative inverse depth

Python (desktop verification)

MEAN = np.array([0.485, 0.456, 0.406], np.float32)
STD  = np.array([0.229, 0.224, 0.225], np.float32)
import numpy as np
from PIL import Image
from ai_edge_litert.interpreter import Interpreter

img = Image.open("photo.jpg").convert("RGB").resize((256, 256))
x = ((np.asarray(img, np.float32) / 255 - MEAN) / STD)[None]      # [1,256,256,3] NHWC

it = Interpreter(model_path="midas_small_256_fp16.tflite"); it.allocate_tensors()
it.set_tensor(it.get_input_details()[0]["index"], x); it.invoke()
d = it.get_tensor(it.get_output_details()[0]["index"])[0]         # [256,256]
d = (d - d.min()) / (d.max() - d.min())                           # near = bright
Image.fromarray((d * 255).astype(np.uint8)).save("depth.png")

Training data & PII

This is a weights-exact format conversion of Intel ISL's MiDaS v2.1 small; no new training was performed. MiDaS was trained for monocular depth on a mix of ~10 public depth datasets (e.g. ReDWeb, DIML, MegaDepth, WSVD, 3D Movies). These contain photos of real scenes that may incidentally include people and other PII; none was deliberately collected and this conversion adds none. The model outputs a relative-depth map only and performs no identification. Apply your own content/PII filtering before deployment. See the original MiDaS repo for dataset details.

License & attribution

• MiDaS weights: MIT (Intel ISL).
• EfficientNet-Lite3 backbone: Apache-2.0.

Original work: Ranftl et al., "Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer" (MiDaS), https://github.com/isl-org/MiDaS.

Reproducing the conversion

A self-contained converter (litert-torch + ai-edge-quantizer) lives in the sample under compiled_model_api/depth_estimation/conversion/:

pip install litert-torch ai-edge-quantizer torch timm matplotlib pillow
python convert_midas_litert.py out 256