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README.md

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+---
+license: apache-2.0
+library_name: litert
+pipeline_tag: image-segmentation
+base_model: xuebinqin/U-2-Net
+tags:
+  - litert
+  - tflite
+  - on-device
+  - android
+  - background-removal
+  - salient-object-detection
+  - image-matting
+  - u2net
+---
+
+# U²-Net — LiteRT (TFLite) GPU, FP16
+
+On-device [LiteRT](https://ai.google.dev/edge/litert) (`.tflite`) conversion of
+**[U²-Net](https://github.com/xuebinqin/U-2-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-torch`](https://github.com/google-ai-edge/ai-edge-torch) **with 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.
+
+## Usage (Android, LiteRT CompiledModel)
+
+```kotlin
+val model = CompiledModel.create(
+    context.assets, "u2net_fp16.tflite",
+    CompiledModel.Options(Accelerator.GPU), null
+)
+val inputs = model.createInputBuffers()
+val outputs = model.createOutputBuffers()
+inputs[0].writeFloat(nchwFloatArray)   // [1,3,320,320]
+model.run(inputs, outputs)
+val mask = outputs[0].readFloat()      // [1,1,320,320] in [0,1]
+```
+
+A complete Android sample (live camera + gallery background removal) is available in
+[google-ai-edge/litert-samples](https://github.com/google-ai-edge/litert-samples).
+
+## Performance
+
+- ~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.
+
+## License & attribution
+
+- License: **Apache-2.0** (© the U²-Net authors,
+  [xuebinqin/U-2-Net](https://github.com/xuebinqin/U-2-Net/blob/master/LICENSE)).
+- This is a format conversion of the official U²-Net weights (no architectural changes);
+  all credit to the original authors.

u2net_fp16.tflite

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