On-device camouflaged object detection running fully on the LiteRT CompiledModel
GPU delegate (no CPU fallback). SINet-V2
(TPAMI 2022) finds objects that blend into their background — hidden animals, concealed
items, defect/polyp-style targets — where ordinary segmentation fails.
SINet-V2 is a pure CNN (Res2Net + conv decoder). It converts fully GPU-compatible (2447/2447
nodes on the delegate, 1 partition; device corr 0.994) with two patches: ZeroPadMaxPool
for the Res2Net stem (-inf PADV2) and align_corners=True → False on the bilinear upsamples.
CPU-exact vs PyTorch (corr 0.997).
Converted with litert-torch (build_sinet.py): loads the Apache-2.0 SINet-V2 (Res2Net)
weights and exports the final camouflage map.
Performance
Measured on a Pixel 8a (Tensor G3, Android 16) with the standard TFLite benchmark_model tool — 10 warm-up runs then 50 timed runs, reported as the tool's mean.
Runtime
Backend
Graph on GPU
Latency
TFLite benchmark_model (TfLiteGpuDelegateV2)
GPU (OpenCL)
471 / 471
51.8 ms
TFLite benchmark_model
CPU (XNNPACK, 4 threads)
—
281.4 ms
Any on-device figure recorded when this model shipped came from a different runtime. It was taken through LiteRT's own CompiledModel accelerator (logcat reports it as LITERT_CL), which is the path the Kotlin sample app and the LiteRT API use, and it appears elsewhere on this card. The rows above are the classic TFLite OpenCL delegate, measured with a tool anyone can download and re-run. The two are not comparable, so read the rows above as a reproducible floor rather than as this model's speed on LiteRT.
Snapdragon NPU (Hexagon)
This file runs on the Qualcomm Hexagon NPU as published — no conversion and no
pre-compiled artifact. LiteRT compiles it on the device and caches the result.
Measured on a physical Samsung Galaxy S26 (Snapdragon 8 Elite Gen 5 / SM8850,
Hexagon v81) with LiteRT CompiledModel 2.2.0 — 5 warm-up runs then 50 timed runs, one
accelerator per process, every row taken at device thermal status NONE.
Compute unit
Inference (median / min)
Load
Start headroom
NPU (Hexagon) — first launch
3.13 ms / 3.04 ms
7358 ms
0.59
NPU (Hexagon) — cached
3.11 ms / 3.05 ms
135 ms
0.59
GPU (Adreno)
12.53 ms / 12.28 ms
1268 ms
0.60
The NPU is 4.0x faster on inference here (3.11 ms against 12.53 ms). The first launch pays once for on-device compilation; every launch after that
loads in 135 ms against 1268 ms for the GPU (9.4x), because the GPU rebuilds its
shaders each time. The file is fp16 and needs no int8 quantization to reach the NPU.
Running it on the NPU
Put these in jniLibs/arm64-v8a/. None of them are distributed from this repository —
the first two come from Google, the rest from Qualcomm's own SDK:
Qualcomm QAIRT — the same zip ships fetch_qualcomm_library.sh, which downloads the SDK and copies them for you
Pick the runtime matching the device's Hexagon version: SM8550 → v73, SM8650 → v75,
SM8750 → v79, SM8850 → v81.
kotlin
1val env = Environment.create(2 context,3mapOf(4 Environment.Option.DispatchLibraryDir to context.applicationInfo.nativeLibraryDir,5// Required for on-device compilation. Without it the model silently runs on CPU.6 Environment.Option.CompilerPluginLibraryDir to context.applicationInfo.nativeLibraryDir,7),8)9val options = CompiledModel.Options(Accelerator.NPU).apply{10 qualcommOptions = CompiledModel.QualcommOptions(11 htpPerformanceMode = CompiledModel.QualcommOptions.HtpPerformanceMode.BURST
12)13}14val model = CompiledModel.create(context.assets,"sinet.tflite", options, env)
Build settings: useLegacyPackaging = true under packaging { jniLibs { … } }, so the
DSP can open the skel from a real path, and Kotlin 2.3+ for LiteRT 2.2.0's metadata.
Every NPU failure here is silent. There is no error when the NPU is unavailable —
you get a plausible CPU number instead. Confirm from logcat which delegate took the
graph: Replacing 1 out of 1 node(s) with delegate (DispatchDelegate) is the NPU,
while ... (TfLiteXNNPackDelegate) is the CPU. A missing library is reported only as
a W-level dlopen failed line under a generic No compiler plugin found summary.
On the conditions. Thermal headroom is reported as measured, where 1.0 is the
throttling threshold. All rows were taken at a comparable headroom and compare directly;
figures taken at a different headroom will differ. Each accelerator ran in its own
process, because LiteRT's Environment is shared within one and the first model load
fixes the options for every later one.
Raspberry Pi 5 (CPU)
Measured on a Raspberry Pi 5 Model B Rev 1.1 (8 GB, Raspberry Pi OS 64-bit) with the LiteRT benchmark_model tool from litert-cli-nightly 0.2.0.dev20260805: CPU inference (XNNPACK, 4 threads), 3 invocations per file of 10 warm-up plus 50 timed runs (the tool caps a phase at 150 s, so very slow graphs run fewer — the Runs column is the actual timed total). The latency is the median across invocations; the spread is the min–max over all timed runs. No thermal throttling occurred during these runs (vcgencmd get_throttled stayed 0x0).
File
Inference (median)
Spread (min–max)
Runs
Peak memory
sinet.tflite
274.7 ms
272.3–322.8 ms
150
253 MB
License
Apache-2.0 (SINet-V2 / GewelsJI). Trained on COD10K.
