Yolox Onnx

--- 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 softwa...

Source context: 0 downloads · 0 likes · Pipeline object-detection · Library onnx · Repo Heliosoph/yolox-onnx