yolox-onnx

YOLOX — ONNX (full size ladder)

ONNX bundle of Megvii-BaseDetection/YOLOX covering all seven published size variants in a single repo. Apache-2.0 licensed alternative to Ultralytics YOLOv8 (which is AGPL-3.0).

Not converted locally — these are the ONNX checkpoints Megvii publishes alongside YOLOX itself.

Credit: Megvii.

What this repo contains

All variants share the same 80-class COCO label set and the same input/output tensor signature — you can swap sizes without rewriting inference code.

How to use

import onnxruntime as ort
import numpy as np

sess = ort.InferenceSession("yolox_s.onnx")

# Input: [1, 3, 640, 640] float32, RGB, NOT normalized (YOLOX expects raw [0,255]).
# Letterbox-pad images to 640x640 maintaining aspect ratio before feeding.
outputs = sess.run(None, {"images": img_tensor})[0]

# Outputs: [1, 8400, 85]
#   8400 = anchor predictions (P3/P4/P5 grids combined)
#   85   = (cx, cy, w, h, obj_conf, *80 class probs)
# Decode by multiplying obj_conf × max(class_probs), apply NMS.

Reference postprocessing in Python and C++: Megvii's YOLOX/demo/ONNXRuntime.

Which one should I use?

• CPU / mobile / NPU: yolox_nano or yolox_tiny
• General-purpose: yolox_s (the recommended default)
• GPU, accuracy matters: yolox_l or yolox_x
• yolox_darknet is included for reproducibility but the CSPDarknet-backbone variants above generally supersede it.

Why not YOLOv8?

YOLOv8 (Ultralytics) is AGPL-3.0 — using it in commercial or closed-source software triggers strong copyleft obligations. YOLOX is Apache-2.0 and roughly comparable on accuracy. The DatumV catalog uses YOLOX as the default detector for this reason.

License

Apache-2.0 — same as upstream. LICENSE file included.