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Heliosoph/yolox-onnx

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face

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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

File	Params	Size	mAP (COCO val)	Inference cost
`yolox_nano.onnx`	0.9M	3.5 MB	25.8	smallest, mobile/edge
`yolox_tiny.onnx`	5.1M	20 MB	32.8	mobile / fast CPU
`yolox_s.onnx`	9.0M	35 MB	40.5	recommended general default
`yolox_m.onnx`	25.3M	97 MB	46.9	balanced
`yolox_l.onnx`	54.2M	207 MB	49.7	quality > speed
`yolox_x.onnx`	99.1M	378 MB	51.1	maximum accuracy
`yolox_darknet.onnx`	63.7M	244 MB	47.7	Darknet backbone variant, mostly historical

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

python

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.