# megvii-basedetection/yolox

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10,504 stars · 2,474 forks · Python · Apache-2.0

## Links

- GitHub: https://github.com/Megvii-BaseDetection/YOLOX
- awesome-repositories: https://awesome-repositories.com/repository/megvii-basedetection-yolox.md

## Topics

`deep-learning` `megengine` `ncnn` `object-detection` `onnx` `openvino` `pytorch` `tensorrt` `yolo` `yolov3` `yolox`

## Description

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

## Tags

### Artificial Intelligence & ML

- [Anchor-Free Detection Models](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-detection-tracking/object-detection/anchor-free-detection-models.md) — Provides an anchor-free YOLO architecture for real-time object detection with multi-backend inference support.
- [Real-Time Object Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-detection-tracking/real-time-object-detection.md) — Runs anchor-free object detection on images or video streams with higher accuracy and faster inference than prior YOLO versions. ([source](https://cdn.jsdelivr.net/gh/megvii-basedetection/yolox@main/README.md))
- [Anchor-Free Detection Logic](https://awesome-repositories.com/f/artificial-intelligence-ml/anchor-box-systems/anchor-free-detection-logic.md) — Implements anchor-free detection logic that regresses object locations directly from center points without predefined anchor boxes.
- [Decoupled Detection Heads](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-models/classifier-head-management/auxiliary-classification-heads/detection-head-registrations/decoupled-detection-heads.md) — Ships a decoupled detection head that separates classification and regression into distinct branches for better accuracy.
- [End-to-End Training Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/end-to-end-training-pipelines.md) — Provides an end-to-end training pipeline from data loading to loss computation without post-processing dependencies.
- [Multi-Backend Inference Support](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-backends/multi-backend-inference-support.md) — Supports running inference across ONNX, TensorRT, ncnn, OpenVINO, and MegEngine backends for deployment flexibility.
- [SimOTA Label Assignments](https://awesome-repositories.com/f/artificial-intelligence-ml/label-assignment-strategies/simota-label-assignments.md) — Assigns positive samples dynamically using a simplified Optimal Transport algorithm for improved training efficiency.
- [Production Inference Exports](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training/model-exporting/production-inference-exports.md) — Exports trained models to production-ready formats for deployment on diverse hardware platforms. ([source](https://cdn.jsdelivr.net/gh/megvii-basedetection/yolox@main/README.md))
- [YOLO Object Detectors](https://awesome-repositories.com/f/artificial-intelligence-ml/video-object-tracking/yolo-object-detectors.md) — Runs real-time object detection using a high-performance anchor-free YOLO architecture on images and video streams.
- [NMS-Free Inference Workflows](https://awesome-repositories.com/f/artificial-intelligence-ml/bounding-box-detection/nms-free-inference-workflows.md) — Eliminates non-maximum suppression during inference to reduce post-processing latency for real-time applications.
- [Edge Object Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-detection-tracking/edge-object-detection.md) — Optimizes lightweight model variants for deployment on resource-constrained edge devices like mobile phones. ([source](https://cdn.jsdelivr.net/gh/megvii-basedetection/yolox@main/README.md))
- [Inference Abstractions](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-abstractions.md) — Wraps model export and inference logic behind a unified API that abstracts hardware-specific optimizations.
- [Cross-Platform Deployments](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/local-and-on-device-inference/edge-ai-model-deployment/cross-platform-deployments.md) — Deploys object detection models across CPU, GPU, and edge devices using multiple inference backends.

### Data & Databases

- [MegEngine Backend Integrations](https://awesome-repositories.com/f/data-databases/table-indexing-systems/search-backends/search-backend-swapping/deep-learning-backend-swapping/megengine-backend-integrations.md) — Integrates MegEngine as the primary deep learning backend for automatic differentiation and model optimization.

### DevOps & Infrastructure

- [Model Inference Deployment](https://awesome-repositories.com/f/devops-infrastructure/deployment-management/model-inference-deployment.md) — Exports trained detection models to ONNX, TensorRT, ncnn, OpenVINO, or MegEngine for production inference. ([source](https://cdn.jsdelivr.net/gh/megvii-basedetection/yolox@main/README.md))
- [Detection Model Exporters](https://awesome-repositories.com/f/devops-infrastructure/deployment-management/model-export-formats/detection-model-exporters.md) — Ships a dedicated export pipeline converting trained detection models to ONNX, TensorRT, ncnn, OpenVINO, and MegEngine formats.

### Graphics & Multimedia

- [Model Export Pipelines](https://awesome-repositories.com/f/graphics-multimedia/video-converters/multi-format-exporters/multi-format-asset-exports/model-export-pipelines.md) — Converts trained models to ONNX, TensorRT, ncnn, OpenVINO, and MegEngine formats for deployment across diverse hardware.
- [Multi-Format](https://awesome-repositories.com/f/graphics-multimedia/video-converters/multi-format-exporters/multi-format-asset-exports/model-export-pipelines/multi-format.md) — Converts trained models to ONNX, TensorRT, ncnn, OpenVINO, and MegEngine formats for diverse hardware backends.

### Part of an Awesome List

- [Computer Vision](https://awesome-repositories.com/f/awesome-lists/ai/computer-vision.md) — High-performance anchor-free object detection.
- [Object Detection Models](https://awesome-repositories.com/f/awesome-lists/ai/object-detection-models.md) — Anchor-free object detection model with high performance.
- [CNN](https://awesome-repositories.com/f/awesome-lists/more/cnn.md) — Listed in the “CNN” section of the Ailia Models awesome list.
- [Object Detection](https://awesome-repositories.com/f/awesome-lists/more/object-detection.md) — Listed in the “Object Detection” section of the The Incredible Pytorch awesome list.