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README.md

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+---
+license: mit
+library_name: litert
+pipeline_tag: image-segmentation
+tags:
+  - litert
+  - tflite
+  - android
+  - on-device
+  - gpu
+  - clothing-segmentation
+  - fashion
+  - virtual-try-on
+  - u2net
+---
+
+# Cloth Segmentation (U²-Net) — LiteRT GPU
+
+On-device **clothing segmentation** running **fully on the LiteRT `CompiledModel` GPU**
+delegate (no CPU fallback). [cloth-segmentation](https://github.com/levindabhi/cloth-segmentation)
+is a U²-Net trained on iMaterialist-Fashion to segment **upper-body / lower-body /
+full-body clothing** — the building block for virtual try-on and fashion apps. ~88 ms/frame
+on a Pixel 8a.
+
+- **Architecture:** U²-Net (RSU nested residual blocks), 4-class head — pure CNN.
+- **Weights:** [levindabhi/cloth-segmentation](https://github.com/levindabhi/cloth-segmentation) (iMaterialist-Fashion) · MIT.
+- **Size:** 176 MB.
+
+![Cloth segmentation](hero.png)
+
+*Upper-body clothing (cyan) + lower-body (orange). Photo: Unsplash (free license).*
+
+## I/O
+
+- **Input:** `[1, 3, 768, 768]` NCHW, RGB, `(x/255 - 0.5)/0.5` (i.e. [-1, 1]).
+- **Output:** `[1, 4, 768, 768]` logits — `argmax` over the 4 classes: 0 = background,
+  1 = upper body, 2 = lower body, 3 = full body (dress).
+
+## GPU conversion
+
+U²-Net is a pure CNN → fully GPU-compatible (**254/254 nodes on the delegate, 1
+partition**; device corr 0.999798, ~88 ms) with **one defensive patch**: `align_corners=True`
+→ `False` on the bilinear upsamples (the GPU delegate rejects `align_corners=True`).
+CPU-exact vs PyTorch (corr 1.0).
+
+## Minimal usage
+
+### Kotlin (Android, LiteRT CompiledModel GPU)
+
+```kotlin
+val options = CompiledModel.Options(Accelerator.GPU)
+val model = CompiledModel.create(context.assets, "clothseg.tflite", options, null)
+val inBufs = model.createInputBuffers()
+val outBufs = model.createOutputBuffers()
+
+inBufs[0].writeFloat(inputNCHW)          // [1,3,768,768] RGB, (x/255-0.5)/0.5
+model.run(inBufs, outBufs)
+val out = outBufs[0].readFloat()         // [4*768*768]; per pixel p argmax over the 4 class planes
+// class 0 bg, 1 upper, 2 lower, 3 full-body
+```
+
+### Python (LiteRT / ai-edge-litert)
+
+```python
+import numpy as np
+from ai_edge_litert.interpreter import Interpreter
+
+it = Interpreter(model_path="clothseg.tflite"); it.allocate_tensors()
+inp, out = it.get_input_details(), it.get_output_details()
+it.set_tensor(inp[0]["index"], x)        # [1,3,768,768] float32, RGB, (x/255-0.5)/0.5
+it.invoke()
+seg = it.get_tensor(out[0]["index"])[0].argmax(0)   # [768,768] 0=bg 1=upper 2=lower 3=full
+```
+
+## Conversion
+
+Converted with **litert-torch** (`build_clothseg.py`): loads the MIT U²-Net cloth weights
+and exports the 4-class graph.
+
+## License
+
+MIT (cloth-segmentation / levindabhi). Trained on iMaterialist-Fashion-2019.