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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.
Procyonスイートの詳細を読む
詳細を見るUL Procyonは、業界、企業、政府、小売業、報道機関のプロフェッショナルユーザー向けに特別に作成しているULの新しいベンチマークスイートです。各Procyonベンチマークには共通の設計と機能が備わっており、なじみのある一貫した体験をお届けします。Procyonベンチマークは、現実世界のさまざまな使用事例でのパフォーマンスを測定します。AI推論、オフィスの生産性、バッテリ寿命、写真編集、動画編集のベンチマークを提供します。