On-device LiteRT (.tflite) conversion of
U²-Net for salient-object segmentation /
background removal. U²-Net is a nested U-structure ("U-net of U-nets", a pure CNN)
that predicts a single-channel saliency mask; the foreground is composited onto
transparency to cut the subject out of its background.
The model runs fully on the LiteRT CompiledModel GPU accelerator (ML Drift):
every op is GPU-native, no CPU fallback, no Flex ops. It converts with
litert-torchwith no custom
rewrites (pure CNN).
Files
File
Size
Description
u2net_fp16.tflite
88 MB
float16 weights, GPU-compatible
I/O
Input: [1, 3, 320, 320] float32, NCHW, RGB. Preprocessing: resize to 320×320,
divide by the per-image max, then ImageNet normalize
(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225]).
Output: [1, 1, 320, 320] saliency mask in [0, 1] (sigmoid). Upscale to the input
size and use as the foreground alpha.
Minimal usage
Android (Kotlin, CompiledModel GPU)
kotlin
1val model = CompiledModel.create(context.assets,"u2net_fp16.tflite",2 CompiledModel.Options(Accelerator.GPU),null)3val inputs = model.createInputBuffers()4val outputs = model.createOutputBuffers()5inputs[0].writeFloat(chw)// [1,3,320,320] /max then ImageNet-norm, NCHW6model.run(inputs, outputs)7val mask = outputs[0].readFloat()// [1,1,320,320] saliency in [0,1]
~147 ms / frame on a Pixel 8a (Tensor G3, Mali) GPU.
Conversion notes
Converted with litert-torch (full U2NET, 44M params) and float16-quantized with
ai-edge-quantizer. Verified: all ops GPU-native, output correlation = 1.0 vs the PyTorch
reference (FP32), ~0.9999 for the FP16 build.
Training data & PII
This is a weights-exact format conversion of the public U²-Net salient-object-detection
model; no new training was performed. U²-Net was trained on the DUTS-TR saliency dataset
(web images with binary salient-object masks). Such web images may incidentally contain
people and other PII; none was deliberately collected and this conversion adds none. The
model outputs a saliency mask only and performs no identification. Apply your own
content/PII filtering before deployment. See the original
U²-Net repo for dataset details.
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
LiteRT CompiledModel (LITERT_CL)
GPU
—
~147 ms
TFLite benchmark_model (TfLiteGpuDelegateV2)
GPU (OpenCL)
374 / 374
117.7 ms
TFLite benchmark_model
CPU (XNNPACK, 4 threads)
—
1797.5 ms
The two GPU rows are different runtimes, not a contradiction. The LITERT_CL figure is the one recorded when this model shipped, taken through LiteRT's own CompiledModel accelerator — the path the Kotlin sample app and the LiteRT API use. The TfLiteGpuDelegateV2 figure is the classic TFLite OpenCL delegate, measured with a tool anyone can download and re-run. They agree on how much of the graph the GPU takes; they disagree on speed, and the classic delegate is the slower of the two here. Read the TfLiteGpuDelegateV2 row as a reproducible floor, not as this model's speed on LiteRT.
Snapdragon NPU (Hexagon)
The NPU is 4.17x faster than the GPU (8.82 ms against 36.80 ms) and loads 11.10x faster (146 ms against 1619 ms).
backend
compiled
inference (median / min)
load
NPU (Hexagon v81)
on-device JIT
8.82 ms / 8.77 ms
146 ms
GPU (Adreno)
—
36.80 ms / 35.76 ms
1619 ms
Measured on a Samsung Galaxy S26 (Snapdragon 8 Elite Gen 5 / SM8850, Hexagon v81, Android 16) with LiteRT CompiledModel 2.2.0, one accelerator per process, 5 warm-up runs then N=50 timed runs, median reported. Every run held thermal status NONE throughout. Headroom 0.82, where 1.0 is the throttling threshold.
The NPU rows ran the published file unchanged. LiteRT compiled it for the Hexagon on the device at first load. That first compile took 5.6 s here. The load column above is the cached load every later run pays. Recipe and the runtime libraries it needs: NPU guide.
