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README.md

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+---
+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
+```

midas_small_256_fp16.tflite

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