| --- |
| license: apache-2.0 |
| library_name: onnx |
| tags: |
| - object-detection |
| - yolox |
| - coco |
| - onnx |
| base_model: Megvii-BaseDetection/YOLOX |
| pipeline_tag: object-detection |
| language: |
| - en |
| --- |
| |
| # YOLOX — ONNX (full size ladder) |
|
|
| ONNX bundle of [Megvii-BaseDetection/YOLOX](https://github.com/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](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/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 DatumIngest catalog uses YOLOX as the default detector for this reason. |
| |
| ## License |
| |
| **Apache-2.0** — same as upstream. `LICENSE` file included. |
