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README.md

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+---
+license: apache-2.0
+library_name: litert
+pipeline_tag: keypoint-detection
+tags:
+  - litert
+  - tflite
+  - on-device
+  - android
+  - pose-estimation
+  - openpose
+  - gpu
+---
+
+# lightweight-OpenPose — LiteRT (TFLite) GPU, FP16
+
+On-device [LiteRT](https://ai.google.dev/edge/litert) (`.tflite`) conversion of
+**[lightweight-OpenPose](https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch)**
+for human pose estimation. The model is a MobileNet-based heatmap network; it outputs
+**keypoint heatmaps only** and the keypoint decode (argmax) is done in app code.
+
+The model runs **fully on the LiteRT `CompiledModel` GPU accelerator** (ML Drift): every op is
+GPU-native, no CPU fallback. Converted with
+[`litert-torch`](https://github.com/google-ai-edge/ai-edge-torch) **with no patches**.
+
+> Why heatmaps-only: MoveNet's official `.tflite` bakes the keypoint decode into the graph
+> (`GATHER_ND`), which the GPU delegate can't run — so it only partially offloads to the GPU.
+> Keeping the graph pure-conv and decoding in app code keeps it 100% on the GPU.
+
+## Files
+
+| File | Precision | Size |
+|------|-----------|------|
+| `pose_256_fp16.tflite` | fp16 weights | ~8.3 MB |
+| `pose_256.tflite` | fp32 | ~16.4 MB |
+
+## I/O
+
+- **Input**: `[1, 256, 256, 3]` float32, **NHWC**, RGB, normalized `(px - 128) / 256`.
+- **Output**: `[1, 32, 32, 19]` float32, **NHWC**, keypoint heatmaps (18 body keypoints +
+  background). Argmax each of the 18 keypoint channels over the `32 x 32` grid to get the
+  normalized keypoint locations; connect them into a skeleton.
+
+Keypoint order (18): nose, neck, r-shoulder, r-elbow, r-wrist, l-shoulder, l-elbow, l-wrist,
+r-hip, r-knee, r-ankle, l-hip, l-knee, l-ankle, r-eye, l-eye, r-ear, l-ear.
+
+## Ops
+
+```
+CONV_2D x41, DEPTHWISE_CONV_2D x14, TRANSPOSE x14, EXP x6, SUB x6,
+GREATER_EQUAL x6, SELECT x6, ADD x6, PAD x3, CONCATENATION x1
+```
+
+(The `ELU` activations lower to `EXP/SUB/GREATER_EQUAL/SELECT`, all GPU-supported.) No
+`GATHER_ND`, no Flex/Custom.
+
+## On-device (Pixel 8a, verified)
+
+The fp16 model compiles to **158 / 158 nodes on the LiteRT GPU delegate (LITERT_CL)** — full
+GPU residency, no CPU fallback.
+
+## Usage (Android, LiteRT CompiledModel)
+
+```kotlin
+val model = CompiledModel.create(
+    context.assets, "pose_256_fp16.tflite",
+    CompiledModel.Options(Accelerator.GPU), null
+)
+val inputs = model.createInputBuffers()
+val outputs = model.createOutputBuffers()
+inputs[0].writeFloat(rgbNormalized)    // [1,256,256,3], (px-128)/256
+model.run(inputs, outputs)
+val heatmaps = outputs[0].readFloat()  // [1,32,32,19] -> argmax per keypoint in app code
+```
+
+A complete Android sample (camera + gallery, skeleton overlay) is available in
+[google-ai-edge/litert-samples](https://github.com/google-ai-edge/litert-samples).
+
+## License & attribution
+
+- License: **Apache-2.0**. Weights/model from
+  [`Daniil-Osokin/lightweight-human-pose-estimation.pytorch`](https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch).
+  Based on *"Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose"* (Osokin,
+  2018). Format conversion only; all credit to the original authors.

pose_256.tflite

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+oid sha256:b5c200e7050f1e17884059bf3da72b14e842af555ad67a49f46a4a9b37aeb0cd
+size 16405352

pose_256_fp16.tflite

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+oid sha256:357c498b310d0116d07b0222d4c62a096bd8ca775ffcb8ed2a4840a1d9b87826
+size 8339040