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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
+  - semantic-segmentation
+  - cityscapes
+  - real-time
+  - pidnet
+---
+
+# PIDNet-S — LiteRT (real-time semantic segmentation, GPU)
+
+On-device **real-time semantic segmentation** running **fully on the LiteRT
+`CompiledModel` GPU** delegate (no CPU fallback). [PIDNet-S](https://arxiv.org/abs/2206.02066)
+(CVPR 2023) segments a road scene into the **19 Cityscapes classes** at ~17 FPS on a
+Pixel 8a.
+
+- **Architecture:** PIDNet-S — a three-branch CNN (P: detail, I: context, D: boundary).
+- **Weights:** [XuJiacong/PIDNet](https://github.com/XuJiacong/PIDNet) · MIT · 78.8% mIoU (Cityscapes val).
+- **Size:** 30 MB · ~7.6 M params · pure CNN.
+
+![PIDNet-S segmentation](hero.png)
+
+## I/O
+
+- **Input:** `[1, 3, 1024, 1024]` NCHW, RGB, ImageNet-normalized
+  (mean `[0.485,0.456,0.406]`, std `[0.229,0.224,0.225]`).
+- **Output:** `[1, 19, 128, 128]` class logits at 1/8 resolution — argmax over the 19
+  classes per pixel, then upscale (nearest) to display.
+
+Classes (index order): `road, sidewalk, building, wall, fence, pole, traffic light,
+traffic sign, vegetation, terrain, sky, person, rider, car, truck, bus, train,
+motorcycle, bicycle`.
+
+## GPU conversion
+
+PIDNet is a pure CNN — no attention, no dynamic shapes at a fixed input size, and
+`align_corners=False` on every bilinear resize. It converts to a **fully
+GPU-compatible graph with zero patches**: `CONV_2D` ×75, `RESIZE_BILINEAR` ×11
+(align_corners=False), `AVERAGE_POOL_2D`, `ADD`/`MUL`/`SUB`/`SUM`, `LOGISTIC` —
+**0 tensors of rank > 4, 0 GPU-incompatible ops**. The converted graph matches the
+original PyTorch model bit-for-bit on CPU (corr 0.99999999999, 100% argmax); on the
+Mali GPU (fp16) it agrees with the fp32 reference at 97% of pixels with correct
+classes.
+
+## Minimal usage
+
+### Kotlin (Android, LiteRT CompiledModel GPU)
+
+```kotlin
+val options = CompiledModel.Options(Accelerator.GPU)
+val model = CompiledModel.create(context.assets, "pidnet_s.tflite", options, null)
+val inBufs = model.createInputBuffers()
+val outBufs = model.createOutputBuffers()
+
+inBufs[0].writeFloat(inputNCHW)              // [1,3,1024,1024], RGB, ImageNet-norm
+model.run(inBufs, outBufs)
+val logits = outBufs[0].readFloat()          // [19,128,128] (NCHW, batch dropped)
+
+// argmax over 19 classes per pixel:
+val hw = 128 * 128
+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="pidnet_s.tflite"); it.allocate_tensors()
+inp, out = it.get_input_details(), it.get_output_details()
+it.set_tensor(inp[0]["index"], x)            # [1,3,1024,1024] float32, ImageNet-norm
+it.invoke()
+logits = it.get_tensor(out[0]["index"])[0]   # [19,128,128]
+label = logits.argmax(0)                      # [128,128] class ids
+```
+
+## Conversion
+
+Re-authored/converted with **litert-torch** (`build_pidnet.py`): the trained PIDNet-S
+weights are loaded from an ONNX mirror whose initializer names match the original
+repo's PyTorch keys, then converted directly — zero GPU patches.
+
+## License
+
+MIT (PIDNet / XuJiacong/PIDNet). Cityscapes label taxonomy from the Cityscapes dataset.