-
软件
-
服务
-
支持
-
见解
-
对比
-
更多
-
UL Procyon AI Quality Metrics
While comparing inference engines in computer vision workloads, it’s also important to consider accuracy in addition to raw performance. Converting the different AI models between formats and quantizing to different precisions to run using different inference engines can affect the quality of object recognition in a given image.
To support comparisons between platforms, we’ve run our own tests measuring the accuracy of inference engines supported by the Procyon AI Computer Vision Benchmark.
The following interactive graph shows the computer vision models & inference engines tested by UL Solutions. Along the X-axis are the accuracies of the tested models, while along the y-axis you can find the AI engines and devices. The data is grouped according to the quality metric specific to the ai use case and precision of the model.
This was tested by UL Benchmarks for the models used in AI Computer Vision Benchmark (v1.5.290 for Windows and v1.0.58 for macOS) and the real world quality of the models may vary depending on the source of the model and test data set.
详细了解 Procyon 套件
了解更多UL Procyon 是 UL 专为工业、企业、政府、零售和新闻行业的专业用户开发的新基准测试套件。每个 Procyon 基准测试将通过共享通用的设计和功能集提供熟悉的一致体验。Procyon 基准测试会测量各种实际用例中的性能。它会提供适用于 AI 推理、办公生产率、电池使用寿命、照片编辑和视频编辑的基准测试。