YOLOX-M — LiteRT (CompiledModel GPU)
Megvii YOLOX-M (COCO, Apache-2.0) re-authored to a GPU-native LiteRT .tflite via the
official litert_torch path (no onnx2tf). FP16, 51.0 MB, input 640×640.
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, 640, 640, 3] NHWC, BGR, 0–255, no normalization (YOLOX letterbox:
uniform-scale to fit, pad bottom/right with gray 114).
- Output
[1, 8400, 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 46.9 (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)
Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.
