yolox-nano-litert

YOLOX-Nano — LiteRT (CompiledModel GPU)

Megvii YOLOX-Nano (COCO, Apache-2.0) re-authored to a GPU-native LiteRT .tflite via the official litert_torch path (no onnx2tf). FP16, 2.2 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 25.8 (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)

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Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.

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