---
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.

## 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/`:

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