---
license: apache-2.0
library_name: litert
pipeline_tag: image-feature-extraction
tags:
  - litert
  - tflite
  - sam2
  - segment-anything
  - image-encoder
  - on-device
  - gpu
base_model: facebook/sam2.1-hiera-tiny
---

# SAM 2.1 (Hiera-Tiny) image encoder — LiteRT GPU

On-device **LiteRT / TFLite** conversion of the **image encoder** of
[**SAM 2.1 Hiera-Tiny**](https://huggingface.co/facebook/sam2.1-hiera-tiny) (Meta, Apache-2.0),
running **fully on the mobile GPU** via the LiteRT `CompiledModel` API (ML Drift / `LITERT_CL` delegate).
The whole graph is GPU-resident — no CPU/XNNPACK fallback ops.

![SAM 2.1 — tap to segment via the LiteRT image encoder (on-device GPU)](samples/sample.png)

This is the heavy backbone of the Segment Anything 2 image path: it turns an RGB image into the
multi-scale feature pyramid that a (small) prompt-encoder + mask-decoder then query per click/box.

| | |
|---|---|
| Task | Image encoder for promptable segmentation (SAM 2 image path) |
| Backbone | Hiera-Tiny (hierarchical ViT, window + global attention) + FPN neck |
| Input | `[1, 3, 1024, 1024]` NCHW float32, ImageNet-normalized |
| Outputs | 3 FPN feature maps: `[1,256,256,256]`, `[1,256,128,128]`, `[1,256,64,64]` |
| Precision / size | FP16, **80 MB** |
| Device | Pixel 8a, LiteRT GPU (`Accelerator.GPU`), **~7 ms / image** |
| Residency | **`Replacing 862 out of 862 node(s) with delegate (LITERT_CL)`** (full, single partition) |

## Preprocessing (must match)

```
resize to 1024x1024 (bilinear)  ->  x/255  ->  (x - mean) / std
mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225]   # ImageNet, RGB, NCHW
```

## GPU-clean conversion (what was re-authored)

Converted with `litert-torch`. SAM 2's Hiera encoder is not GPU-clean out of the box; these exact,
weights-faithful rewrites were applied (model-side only — **no converter patch**):

1. **`window_partition` / `window_unpartition`**: the 6-D `view`+`permute` window reshape rejected by the
   GPU delegate (>4-D) is re-expressed as a sequence of **≤4-D** `reshape`/`transpose` ops (numerically
   exact, verified vs the original).
2. **`Sam2MultiScaleAttention`**: the 5-D fused-QKV reshape is decomposed into separate q/k/v, and
   attention runs as a **3-D batched SDPA** (`[B*heads, N, d]`). A 4-D SDPA makes the delegate emit a
   `[C,C]->[nW,ws,C,C]` `BROADCAST_TO` on every windowed block; the 3-D form removes all 9.
3. **Windowed positional embedding**: the bicubic-interpolate + tile of the constant `pos_embed` is
   **baked to a buffer** (add only) — removes a runtime interpolate of a constant.
4. **Neck**: the (constant, shape-only) sine FPN position encodings are dropped from the graph (compute
   them host-side) — removes the remaining `BROADCAST_TO` ops.
5. **Overflow-safe LayerNorm** (scale-before-square) as an fp16 safety margin for the deep stages.

Net: `banned ops = NONE`, `>4-D tensors = 0`, full GPU residency.

## Fidelity (honest)

Eager re-authoring is **numerically exact** (`cos = 1.000`, `mae = 0`). On-device GPU output vs the
CPU reference, per FPN level:

| Output | cosine |
|---|---|
| FPN-0 `256x256` (high-res, drives mask detail) | **0.99998** |
| FPN-1 `128x128` | **0.99994** |
| FPN-2 `64x64` (coarse image embedding) | **0.99253** |

The deepest 64×64 feature drifts slightly on the GPU. This is **not** LayerNorm overflow
(scale-before-square LayerNorm doesn't change it, and the CPU fp16 model matches PyTorch fp32 at
corr 0.999999) — it is the mobile GPU computing the deep-stage global attention (64×64 = 4096 tokens)
in true fp16, where the CPU path upcasts to fp32. The high-resolution features that carry mask
boundaries are near-exact, so mask quality is preserved in practice.

## Minimal usage

**Android (Kotlin, CompiledModel GPU)**

```kotlin
val model = CompiledModel.create(context.assets, "sam2_tiny_image_encoder_fp16.tflite",
    CompiledModel.Options(Accelerator.GPU), null)
val inputs = model.createInputBuffers()
val outputs = model.createOutputBuffers()
inputs[0].writeFloat(chw)              // [1,3,1024,1024] ImageNet-normalized, NCHW
model.run(inputs, outputs)
// FPN maps: [1,256,256,256], [1,256,128,128], [1,256,64,64] -> SAM 2 prompt/mask decoder
```

**Python (desktop verification)**

```python
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((1024, 1024))
x = ((np.asarray(img, np.float32) / 255 - MEAN) / STD).transpose(2, 0, 1)[None]

it = Interpreter(model_path="sam2_tiny_image_encoder_fp16.tflite"); it.allocate_tensors()
it.set_tensor(it.get_input_details()[0]["index"], x); it.invoke()
o = {tuple(d["shape"]): it.get_tensor(d["index"]) for d in it.get_output_details()}
fpn0, fpn1, fpn2 = o[(1,256,256,256)], o[(1,256,128,128)], o[(1,256,64,64)]
# feed to the SAM 2.1 Hiera-Tiny mask decoder (companion repo) for tap-to-segment;
# the v2 file emits decoder-ready features directly (see the variant note below)
```

## Training data & PII

SAM 2 was trained by Meta on **SA-1B** (licensed photos) and **SA-V** (licensed videos) with
model-in-the-loop mask annotation. No new training was performed for this conversion — it is a
weights-faithful format change of the public `facebook/sam2.1-hiera-tiny` checkpoint. Because the
source data is real-world imagery, it may incidentally contain people, faces, vehicles, signage and
other PII; no PII was deliberately collected and this conversion adds none. Apply your own content/PII
filtering as appropriate. See the [SAM 2 release](https://github.com/facebookresearch/sam2) and
[paper](https://arxiv.org/abs/2408.00714) for full dataset details.

## License

Apache-2.0, inherited from the upstream [SAM 2.1](https://huggingface.co/facebook/sam2.1-hiera-tiny).
This is a format conversion; all credit to the original authors (Meta AI).


## Variant: decoder-ready (`sam2_tiny_image_encoder_v2_fp16.tflite`)

A second file in this repo, `sam2_tiny_image_encoder_v2_fp16.tflite`, additionally folds the SAM 2 mask
decoder's `conv_s0` (256→32) / `conv_s1` (256→64) projections and the `no_memory` embedding into the
graph, so it directly emits **decoder-ready** features:
`image_embeddings [1,256,64,64]`, `feat_s1 [1,64,128,128]`, `feat_s0 [1,32,256,256]`. Pair it with the
[**SAM 2.1 Hiera-Tiny mask decoder**](https://huggingface.co/litert-community/SAM2.1-Hiera-Tiny-Mask-Decoder)
for promptable "tap to segment" (see the LiteRT `interactive_segmentation` sample). Same GPU-clean
re-authoring and fidelity as the base encoder above; FP16, ~80 MB, full LITERT_CL residency (867/867).