YOLOX is a high-performance object detector developed by Megvii Technology, which improves upon the YOLO series by introducing several advanced techniques. The key innovations include an anchor-free detection approach, a decoupled head, and an advanced label assignment strategy called SimOTA. These improvements allow YOLOX to achieve state-of-the-art results across a wide range of model sizes. For example, YOLOX-Nano, with only 0.91M parameters and 1.08G FLOPs, achieves 25.3% AP on COCO, surpassing NanoDet by 1.8% AP. YOLOv3 is boosted to 47.3% AP on COCO, outperforming the current best practice by 3.0% AP. YOLOX-L achieves 50.0% AP on COCO at 68.9 FPS on Tesla V100, exceeding YOLOv5-L by 1.8% AP. Additionally, YOLOX-L won first place in the Streaming Perception Challenge (WAD at CVPR 2021). YOLOX is based on YOLOv3 with DarkNet53 as the backbone and incorporates a decoupled head, which separates the classification and regression tasks, leading to faster convergence and better performance. The model also uses an anchor-free approach, which eliminates the need for pre-defined anchors and reduces the number of design parameters. This approach significantly improves the detection performance and efficiency. In addition to the anchor-free approach, YOLOX employs advanced label assignment strategies, such as SimOTA, which optimizes the matching between ground-truth objects and predicted bounding boxes. This strategy improves the detection performance by 3.0% AP compared to the current best practice. YOLOX also uses strong data augmentation techniques, such as Mosaic and MixUp, to further enhance the model's performance. YOLOX is available in multiple versions, including YOLOX-Tiny and YOLOX-Nano, which are optimized for mobile devices. These smaller models achieve high performance with minimal computational resources. The YOLOX series is also supported by various deployment frameworks, including ONNX, TensorRT, NCNN, and OpenVino. In conclusion, YOLOX represents a significant advancement in the YOLO series, offering improved performance, efficiency, and flexibility across a wide range of model sizes and deployment scenarios. The model's innovative techniques and optimizations make it a powerful tool for object detection in various practical applications.YOLOX is a high-performance object detector developed by Megvii Technology, which improves upon the YOLO series by introducing several advanced techniques. The key innovations include an anchor-free detection approach, a decoupled head, and an advanced label assignment strategy called SimOTA. These improvements allow YOLOX to achieve state-of-the-art results across a wide range of model sizes. For example, YOLOX-Nano, with only 0.91M parameters and 1.08G FLOPs, achieves 25.3% AP on COCO, surpassing NanoDet by 1.8% AP. YOLOv3 is boosted to 47.3% AP on COCO, outperforming the current best practice by 3.0% AP. YOLOX-L achieves 50.0% AP on COCO at 68.9 FPS on Tesla V100, exceeding YOLOv5-L by 1.8% AP. Additionally, YOLOX-L won first place in the Streaming Perception Challenge (WAD at CVPR 2021). YOLOX is based on YOLOv3 with DarkNet53 as the backbone and incorporates a decoupled head, which separates the classification and regression tasks, leading to faster convergence and better performance. The model also uses an anchor-free approach, which eliminates the need for pre-defined anchors and reduces the number of design parameters. This approach significantly improves the detection performance and efficiency. In addition to the anchor-free approach, YOLOX employs advanced label assignment strategies, such as SimOTA, which optimizes the matching between ground-truth objects and predicted bounding boxes. This strategy improves the detection performance by 3.0% AP compared to the current best practice. YOLOX also uses strong data augmentation techniques, such as Mosaic and MixUp, to further enhance the model's performance. YOLOX is available in multiple versions, including YOLOX-Tiny and YOLOX-Nano, which are optimized for mobile devices. These smaller models achieve high performance with minimal computational resources. The YOLOX series is also supported by various deployment frameworks, including ONNX, TensorRT, NCNN, and OpenVino. In conclusion, YOLOX represents a significant advancement in the YOLO series, offering improved performance, efficiency, and flexibility across a wide range of model sizes and deployment scenarios. The model's innovative techniques and optimizations make it a powerful tool for object detection in various practical applications.