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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
+  - face-parsing
+  - face-segmentation
+  - bisenet
+  - celebamask-hq
+  - real-time
+---
+
+# BiSeNet Face Parsing — LiteRT (GPU)
+
+On-device **real-time face parsing** running **fully on the LiteRT `CompiledModel`
+GPU** delegate (no CPU fallback). [BiSeNet](https://arxiv.org/abs/1808.00897)
+(zllrunning/face-parsing.PyTorch) segments a face into the **19 CelebAMask-HQ
+classes** (skin, brows, eyes, nose, lips, ears, hair, hat, glasses, neck, cloth, …)
+— for AR / beauty / makeup. ~22 ms/frame on a Pixel 8a.
+
+- **Architecture:** BiSeNet (ResNet18 backbone + context path + feature-fusion) — pure CNN.
+- **Weights:** [zllrunning/face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch) · MIT · ~13.3 M params.
+- **Size:** 53 MB.
+
+![BiSeNet face parsing](hero.png)
+
+## I/O
+
+- **Input:** `[1, 3, 512, 512]` NCHW, RGB, ImageNet-normalized
+  (mean `[0.485,0.456,0.406]`, std `[0.229,0.224,0.225]`).
+- **Output:** `[1, 19, 512, 512]` class logits — argmax over the 19 classes per pixel.
+
+Classes: `background, skin, l_brow, r_brow, l_eye, r_eye, eyeglass, l_ear, r_ear,
+earring, nose, mouth, u_lip, l_lip, neck, necklace, cloth, hair, hat`.
+
+## GPU conversion
+
+BiSeNet is a pure CNN; three re-authoring patches make it a **fully GPU-compatible
+graph — 74/74 nodes on the delegate, 1 partition** (device corr 0.99999, argmax
+99.96% vs PyTorch):
+
+1. **`align_corners=True` → `False`** — the output upsamples use `align_corners=True`,
+   which the GPU delegate rejects (1.6% argmax change vs original).
+2. **global `avg_pool2d(x, x.size()[2:])` → `mean([2,3])`** — the context/attention
+   modules pool with a full-spatial kernel, which the Mali delegate rejects as an
+   `AVERAGE_POOL_2D`; a MEAN reduce is supported.
+3. **zero-pad maxpool** — the ResNet stem `MaxPool2d(padding=1)` lowers to a PADV2
+   with `-inf` padding (`PADV2: src has wrong size` on Mali); an explicit 0-pad +
+   unpadded maxpool is exact (input is post-ReLU ≥ 0).
+
+CPU-exact vs PyTorch (corr 0.99999999999).
+
+## Minimal usage
+
+### Kotlin (Android, LiteRT CompiledModel GPU)
+
+```kotlin
+val options = CompiledModel.Options(Accelerator.GPU)
+val model = CompiledModel.create(context.assets, "faceparsing.tflite", options, null)
+val inBufs = model.createInputBuffers()
+val outBufs = model.createOutputBuffers()
+
+inBufs[0].writeFloat(inputNCHW)          // [1,3,512,512], RGB, ImageNet-norm
+model.run(inBufs, outBufs)
+val logits = outBufs[0].readFloat()      // [19,512,512] (NCHW, batch dropped)
+
+val hw = 512 * 512
+val label = IntArray(hw) { i ->
+    var best = 0; var bv = logits[i]
+    for (c in 1 until 19) { val v = logits[c * hw + i]; if (v > bv) { bv = v; best = c } }
+    best
+}
+```
+
+### Python (LiteRT / ai-edge-litert)
+
+```python
+from ai_edge_litert.interpreter import Interpreter
+import numpy as np
+
+it = Interpreter(model_path="faceparsing.tflite"); it.allocate_tensors()
+inp, out = it.get_input_details(), it.get_output_details()
+it.set_tensor(inp[0]["index"], x)        # [1,3,512,512] float32, ImageNet-norm
+it.invoke()
+logits = it.get_tensor(out[0]["index"])[0]   # [19,512,512]
+label = logits.argmax(0)                      # [512,512] class ids
+```
+
+## Conversion
+
+Converted with **litert-torch** (`build_faceparsing.py`): loads the trained BiSeNet
+weights, applies the three patches, and exports.
+
+## License
+
+MIT (BiSeNet / zllrunning/face-parsing.PyTorch). CelebAMask-HQ label taxonomy.