---
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)