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README.md

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+---
+license: apache-2.0
+library_name: litert
+pipeline_tag: object-detection
+tags:
+- object-detection
+- yolox
+- litert
+- tflite
+- on-device
+- gpu
+---
+
+# YOLOX-Tiny — LiteRT (CompiledModel GPU)
+
+Megvii **YOLOX-Tiny** (COCO, Apache-2.0) re-authored to a **GPU-native** LiteRT `.tflite` via the
+official **litert_torch** path (no onnx2tf). FP16, **10.4 MB**, input **416×416**.
+
+Verified on a Pixel 8a: the whole graph runs on the GPU delegate (full **LITERT_CL residency**,
+zero CPU fallback) and the GPU output matches the CPU/PyTorch reference (corr ≥ 0.999).
+
+## Why this is GPU-clean
+
+YOLOX is a pure CNN, but its **Focus stem** (stride-2 space-to-depth slicing) lowers to
+`GATHER_ND`, which the GPU delegate rejects. Here the Focus + its following 3×3 conv are folded
+into a single, numerically-exact **6×6 stride-2 conv**, so the graph has **zero GATHER/GATHER_ND/
+TopK/Cast** ops and **no >4D tensors**. Activations (SiLU) lower to LOGISTIC+MUL.
+
+## I/O
+
+- **Input** `images` `[1, 416, 416, 3]` NHWC, **BGR, 0–255, no normalization** (YOLOX letterbox:
+  uniform-scale to fit, pad bottom/right with gray 114).
+- **Output** `[1, 3549, 85]` raw heads, anchor-major. `85 = 4 box (cx,cy,w,h, grid units) + 1 obj
+  + 80 class`. obj/class are already sigmoid'd; boxes are **not** decoded.
+
+## Host-side decode (kept out of the graph for GPU-cleanliness)
+
+For anchor `i` at grid `(gx,gy)` with `stride ∈ {8,16,32}`:
+`cx=(raw_cx+gx)*stride`, `cy=(raw_cy+gy)*stride`, `w=exp(raw_w)*stride`, `h=exp(raw_h)*stride`;
+`score = obj * max_class`; then per-class NMS. Divide boxes by the letterbox ratio to map back.
+Reference Kotlin + Python decode in the sample below.
+
+## Performance
+
+COCO val2017 AP **32.8** (FP32 reference). Real-time on Pixel 8a GPU.
+
+## Training data & PII
+
+Trained by Megvii on **COCO 2017** (train2017), a public academic object-detection dataset
+(Creative Commons). COCO images contain people as one of the 80 object categories; no names,
+identities, or other personal attributes are modeled or output — the model emits only class id +
+box. No additional or private data was used. Weights are the official Megvii release; only the op
+graph was re-authored for GPU (weights unchanged).
+
+## Sample app + conversion script
+
+Android sample (CompiledModel GPU, Kotlin decode + NMS) and the `litert_torch` conversion script:
+https://github.com/google-ai-edge/litert-samples (compiled_model_api/object_detection)