Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,81 @@
+---
+license: apache-2.0
+library_name: litert
+pipeline_tag: image-classification
+tags:
+  - litert
+  - tflite
+  - android
+  - on-device
+  - gpu
+  - face-anti-spoofing
+  - liveness
+  - presentation-attack-detection
+  - face
+---
+
+# Silent-Face Anti-Spoofing (MiniFASNetV2) — Face liveness (LiteRT GPU)
+
+On-device **face liveness / anti-spoofing** running **fully on the LiteRT `CompiledModel`
+GPU** delegate (no CPU fallback). [Silent-Face-Anti-Spoofing](https://github.com/minivision-ai/Silent-Face-Anti-Spoofing)
+detects **presentation attacks** — a printed photo or a replayed screen shown to the camera
+— so a live face passes and a fake is rejected. The anti-fraud building block for face
+login / e-KYC. Tiny (**1.85 MB**), ~5 ms/frame on a Pixel 8a.
+
+- **Architecture:** MiniFASNetV2 (depthwise-separable CNN) — pure CNN.
+- **Weights:** [minivision-ai/Silent-Face-Anti-Spoofing](https://github.com/minivision-ai/Silent-Face-Anti-Spoofing) (`2.7_80x80_MiniFASNetV2`) · Apache-2.0.
+- **Size:** 1.85 MB.
+
+![Silent-Face liveness](hero.png)
+
+*A photographed face is correctly flagged as a replay/presentation attack. A live camera capture scores "live". Portrait: Unsplash (free license).*
+
+## I/O
+
+- **Input:** `[1, 3, 80, 80]` NCHW, **BGR**, `x/255` — a face crop (~2.7× the face box, centered).
+- **Output:** `[1, 3]` softmax — **class 1 = live/real**, classes 0 & 2 = spoof (print / replay).
+  Live score = `output[1]`; `argmax == 1` → live.
+
+## GPU conversion
+
+MiniFASNetV2 is a pure CNN → fully GPU-compatible (**168/168 nodes on the delegate, 1
+partition**; device corr 1.0, ~5 ms) with **zero patches** (PReLU lowers to GPU-clean
+relu ops). 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, "silentface.tflite", options, null)
+val inBufs = model.createInputBuffers()
+val outBufs = model.createOutputBuffers()
+
+inBufs[0].writeFloat(faceCropNCHW)       // [1,3,80,80] BGR, x/255
+model.run(inBufs, outBufs)
+val p = outBufs[0].readFloat()           // [3] softmax; live = p[1], spoof if argmax != 1
+```
+
+### Python (LiteRT / ai-edge-litert)
+
+```python
+import numpy as np
+from ai_edge_litert.interpreter import Interpreter
+
+it = Interpreter(model_path="silentface.tflite"); it.allocate_tensors()
+inp, out = it.get_input_details(), it.get_output_details()
+it.set_tensor(inp[0]["index"], x)        # [1,3,80,80] float32, BGR, x/255
+it.invoke()
+p = it.get_tensor(out[0]["index"])[0]    # [3]; live = p[1]
+```
+
+## Note
+
+The original repo ensembles two MiniFASNet models (crops at scale 2.7 and 4.0) with a face
+detector; this ships the primary `MiniFASNetV2` (2.7). For best accuracy, feed a detected
+face crop and optionally ensemble the second model.
+
+## License
+
+Apache-2.0 (Silent-Face-Anti-Spoofing / minivision-ai).