README.md

---
license: bsd-3-clause
library_name: LiteRT
pipeline_tag: object-detection
tags: [litert, tflite, on-device, android, gpu, face-detection, yunet, libfacedetection, landmarks]
base_model: ShiqiYu/libfacedetection
---

# YuNet — LiteRT (on-device face detection, fully-GPU)

[YuNet](https://github.com/ShiqiYu/libfacedetection) (ShiqiYu/libfacedetection), a tiny fast face detector
(faces + 5 landmarks), converted to **LiteRT** and running **fully on the `CompiledModel` GPU** (ML Drift) on
Android. **0.076 M params / 0.3 MB fp16.**

![YuNet — face boxes + 5 landmarks on-device LiteRT GPU](samples/sample.png)

## On-device (Pixel 8a, Tensor G3 — verified)

| | |
|---|---|
| nodes on GPU | **146 / 146** LITERT_CL (full residency) |
| inference | **~4 ms** (640×640) |
| size | **0.3 MB** (fp16) |
| accuracy | device-vs-PyTorch corr **0.9999** (all 12 outputs) |

```
image[1,3,640,640] (BGR, 0-255) →[GPU: YuNet]→ 12 outputs: cls/obj/bbox/kps × strides {8,16,32}
```

## How it converts (litert-torch) — clean, no re-authoring

Pure CNN (depthwise-separable `ConvDPUnit`) + a **nearest-upsample** neck (`F.interpolate(mode="nearest")` →
`RESIZE_NEAREST_NEIGHBOR`, no transposed conv) + non-padded `MaxPool2d` (no `PADV2`). The head's per-stride
`permute/reshape/sigmoid` is baked in → 12 decode-ready outputs. Banned ops NONE, ≤4D, tflite-vs-torch corr
**1.0**, device-vs-torch corr **0.9999**.

## Decode (host-side) & preprocessing

**Preprocessing**: letterbox to 640×640, **BGR, 0-255, no normalization**. Anchor-free priors
(`px=col·s, py=row·s`, offset 0): score=`cls·obj`, box=center+`exp(wh)·s`, 5 landmarks `kps·s+prior`, then NMS.

## Minimal usage

**Android (Kotlin, CompiledModel GPU)**

```kotlin
val model = CompiledModel.create(context.assets, "yunet_fp16.tflite",
    CompiledModel.Options(Accelerator.GPU), null)
val inputs = model.createInputBuffers(); val outputs = model.createOutputBuffers()
inputs[0].writeFloat(bgr)           // [1,3,640,640] NCHW BGR, 0-255 (no normalization)
model.run(inputs, outputs)
// 12 outputs in order: cls x3 [1,N,1], obj x3 [1,N,1], bbox x3 [1,N,4], kps x3 [1,N,10]
// for strides {8,16,32}, N = (640/stride)^2 = 6400/1600/400. Decode = Python below.
val cls8 = outputs[0].readFloat()
```

**Python (desktop verification)**

```python
import math, numpy as np
from PIL import Image
from ai_edge_litert.interpreter import Interpreter

im = Image.open("faces.jpg").convert("RGB").resize((640, 640))
bgr = np.asarray(im, np.float32)[:, :, ::-1]                      # BGR, 0-255
x = bgr.transpose(2, 0, 1)[None].copy()                           # [1,3,640,640]

it = Interpreter(model_path="yunet_fp16.tflite"); it.allocate_tensors()
it.set_tensor(it.get_input_details()[0]["index"], x); it.invoke()
o = [it.get_tensor(d["index"])[0] for d in it.get_output_details()]
# output order: cls x3, obj x3, bbox x3, kps x3 (strides 8, 16, 32)
dets = []
for li, s in enumerate([8, 16, 32]):
    cls, obj, bb, kp = o[li][:, 0], o[3 + li][:, 0], o[6 + li], o[9 + li]
    fw = 640 // s
    for i in np.where(cls * obj > 0.6)[0]:                        # score threshold
        px, py = (i % fw) * s, (i // fw) * s
        cx, cy = bb[i, 0] * s + px, bb[i, 1] * s + py
        w, h = math.exp(bb[i, 2]) * s, math.exp(bb[i, 3]) * s
        lm = [(kp[i, 2 * j] * s + px, kp[i, 2 * j + 1] * s + py) for j in range(5)]
        dets.append(([cx - w/2, cy - h/2, cx + w/2, cy + h/2], float(cls[i] * obj[i]), lm))

def iou(a, b):                                                    # greedy NMS, IoU 0.45
    ix = max(0, min(a[2], b[2]) - max(a[0], b[0])); iy = max(0, min(a[3], b[3]) - max(a[1], b[1]))
    u = (a[2]-a[0])*(a[3]-a[1]) + (b[2]-b[0])*(b[3]-b[1]) - ix*iy
    return ix * iy / u if u > 0 else 0
dets.sort(key=lambda d: -d[1]); faces = []
for d in dets:
    if all(iou(d[0], f[0]) < 0.45 for f in faces): faces.append(d)
for box, score, lm in faces: print(f"face {score:.2f}", np.round(box, 1), "landmarks", np.round(lm, 1))
```

## License

[BSD-3-Clause](https://github.com/ShiqiYu/libfacedetection/blob/master/LICENSE). Upstream:
[ShiqiYu/libfacedetection](https://github.com/ShiqiYu/libfacedetection).