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English(EN) Benchmarking Edge Inference Strategies for Deep Learning Models in Industrial Machine Vision

工业机器视觉边缘推理框架基准测试 · 跟踪 2 个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新的研究论文对四种流行框架——PyTorch、ONNX Runtime、OpenVINO 和 TensorRT——在工业机器视觉边缘设备上的深度学习推理性能进行了基准测试。研究发现，OpenVINO 在 CPU 上提供了最快的推理时间，而 TensorRT 在 GPU 上效率最高。然而，在评估基于 Transformer 的视觉模型时，TensorRT 的性能并未优于 PyTorch。 AI

影响 为工业应用的边缘设备上的深度学习模型性能优化提供了见解。

排序理由 在 arXiv 上发表的研究论文，详细介绍了推理框架的基准测试。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

工业机器视觉边缘推理框架基准测试 · 跟踪 2 个来源

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在 arXiv 上发表的研究论文，详细介绍了推理框架的基准测试。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Miguel Gomez Fernandez, David Castro Boga, Roi Mendez-Rial, Eric Lopez-Lopez ·

    工业机器视觉中深度学习模型的边缘推理策略基准测试

    arXiv:2607.11356v1 Announce Type: new Abstract: Edge deployment is often the preferred solution for industrial machine vision systems when low latency, data security, or limited connectivity are critical requirements. Several frameworks are available to optimise inference on edge…

  2. arXiv cs.CV TIER_1 English(EN) · Eric Lopez-Lopez ·

    工业机器视觉中深度学习模型的边缘推理策略基准测试

    Edge deployment is often the preferred solution for industrial machine vision systems when low latency, data security, or limited connectivity are critical requirements. Several frameworks are available to optimise inference on edge devices; however, relatively few studies have s…

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