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YOLOX-PAI: An Improved YOLOX, Stronger and Faster than YOLOv6

arXiv 2208.13040 ↗First posted 27 Aug 2022cs.CVcited by 0

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Abstract (arXiv)

We develop an all-in-one computer vision toolbox named EasyCV to facilitate the use of various SOTA computer vision methods. Recently, we add YOLOX-PAI, an improved version of YOLOX, into EasyCV. We conduct ablation studies to investigate the influence of some detection methods on YOLOX. We also provide an easy use for PAI-Blade which is used to accelerate the inference process based on BladeDISC and TensorRT. Finally, we receive 42.8 mAP on COCO dateset within 1.0 ms on a single NVIDIA V100 GPU, which is a bit faster than YOLOv6. A simple but efficient predictor api is also designed in EasyCV to conduct end2end object detection. Codes and models are now available at: https://github.com/alibaba/EasyCV.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 8 references that are in the map. First posted on arXiv 27 Aug 2022.

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References · 7

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
  3. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 520
  4. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  5. 2021TOOD: Task-aligned One-stage Object Detectioncited by 218
  6. 2022PP-YOLOE: An evolved version of YOLOcited by 75
  7. 2019Learning Spatial Fusion for Single-Shot Object Detectioncited by 59

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