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Benchmarking edge inference frameworks for industrial machine vision · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

A new research paper benchmarks the performance of four popular frameworks—PyTorch, ONNX Runtime, OpenVINO, and TensorRT—for deep learning inference on edge devices in industrial machine vision. The study found that OpenVINO offered the fastest inference times on CPUs, while TensorRT was most efficient on GPUs. However, TensorRT did not outperform PyTorch when evaluating a transformer-based vision model. AI

IMPACT Provides insights into optimizing deep learning model performance on edge devices for industrial applications.

RANK_REASON Research paper published on arXiv detailing benchmarking of inference frameworks.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Benchmarking edge inference frameworks for industrial machine vision · 2 sources tracked

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Research paper published on arXiv detailing benchmarking of inference frameworks.
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COVERAGE [2]

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

    Benchmarking Edge Inference Strategies for Deep Learning Models in Industrial Machine Vision

    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 ·

    Benchmarking Edge Inference Strategies for Deep Learning Models in Industrial Machine Vision

    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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