Megvii-BaseDetection/YOLOX

Health v2 · maintenance only

Adoption not part of the score

10587 stars · 2536 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

YOLOX is a high-performance anchor-free YOLO object detection model family implemented in PyTorch, with pretrained weights and export support for ONNX, TensorRT, ncnn, OpenVINO, and MegEngine. It bridges research and industrial use with benchmarked models from nano to extra-large sizes.

Use cases

• train a custom object detection model on my own dataset
• detect objects in images or video in real time
• export a YOLO model to ONNX or TensorRT for deployment
• run object detection on edge devices with ncnn or OpenVINO
• compare anchor-free YOLO performance against YOLOv5
• fine-tune pretrained YOLOX weights for my use case

When to choose

• you need state-of-the-art anchor-free YOLO detection with pretrained weights
• you want flexible deployment across ONNX, TensorRT, ncnn, and OpenVINO
• you need a PyTorch codebase that is easy to train and fine-tune

When to avoid

• you need segmentation or pose estimation rather than bounding-box detection
• you want a maintained one-command CLI detector rather than a research codebase
• you work outside Python/PyTorch and only need a prebuilt inference binary

Facets

library · maturity stable

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning artificial-intelligence python cross-platform object-detection yolo anchor-free pytorch onnx tensorrt ncnn openvino megengine model-zoo inference gpu linux docker

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markdown · JSON · MCP: product_card(name="Megvii-BaseDetection/YOLOX")