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README.md

@@ -56,7 +56,30 @@ raw outputs) with **one patch**: the ResNet50 stem `MaxPool2d(padding=1)` lowers
 post-ReLU). The scripted FPN is made traceable by disabling YOLACT's JIT
 (`use_jit=False`). CPU-exact vs PyTorch (corr 1.0).
 
-## Minimal usage (Python, LiteRT / ai-edge-litert)
+## Minimal usage
+
+### Kotlin (Android, LiteRT CompiledModel GPU)
+
+```kotlin
+val options = CompiledModel.Options(Accelerator.GPU)
+val model = CompiledModel.create(context.assets, "yolact.tflite", options, null)
+val inBufs = model.createInputBuffers()
+val outBufs = model.createOutputBuffers()   // map by size: loc=N*4, conf=N*81, mask=N*32, proto=138*138*32
+
+inBufs[0].writeFloat(inputNCHW)              // [1,3,550,550] BGR, (x-[103.94,116.78,123.68])/[57.38,57.12,58.40]
+model.run(inBufs, outBufs)
+val loc = outBufs[iLoc].readFloat()          // [19248*4]
+val conf = outBufs[iConf].readFloat()        // [19248*81] (softmax)
+val mask = outBufs[iMask].readFloat()        // [19248*32] coefficients
+val proto = outBufs[iProto].readFloat()      // [138*138*32] prototypes
+
+// host-side decode (priors.bin bundled as an asset):
+//   box = SSD-decode(loc, priors, variances=[0.1,0.2]); per-class NMS (score 0.3, IoU 0.5);
+//   per kept det: mask = sigmoid(proto @ coeff) (>0) cropped to the box.
+// Full implementation: YolactSegmenter.kt in the sample app.
+```
+
+### Python (LiteRT / ai-edge-litert)
 
 ```python
 import numpy as np
@@ -77,8 +100,6 @@ boxes = np.concatenate([cxy - wh / 2, cxy + wh / 2], 1)      # x1y1x2y2 (0..1)
 # then per-class NMS on conf, and mask_i = sigmoid(proto @ mask[i]) cropped to boxes[i]
 ```
 
-A full Android decode is in the sample app (`YolactSegmenter.kt`).
-
 ## License
 
 MIT (YOLACT / dbolya/yolact). COCO class taxonomy.