Section 14: ICNet, Image Segmentation,.Section 13: Nvidia-SPADE, Image-to-Image Mapping,.Section 12: U-Net, Image-to-Image Mapping,.Section 11: ResNet-DPED, Image-to-Image Mapping,.Section 10: ResNet-SRGAN, Image-to-Image Mapping,.Section 9: VGG-19, Image-to-Image Mapping,.Section 8: SRCNN 9-5-5, Image-to-Image Mapping,.Section 6: ResNet-V2-152, Classification,.Section 5: ResNet-V2-50, Classification,.Section 4: Inception-ResNet-V2, Classification,.Section 3: Inception-V4, Classification,.Section 2: Inception-V3, Classification,.Section 1: MobileNet-V2, Classification,.In total, AI Benchmark consists of 42 tests and 19 sections provided below:

AI Benchmark is currently distributed as a Python pip package and can be downloaded to any system running Windows, Linux or macOS. The benchmark is relying on TensorFlow machine learning library, and is providing a precise and lightweight solution for assessing inference and training speed for key Deep Learning models. Measuring AI Performance of Desktop CPUs and GPUs AI Benchmark Alpha is an open source python library for evaluating AI performance of various hardware platforms, including CPUs, GPUs and TPUs. Today we are making a step forward towards standardizing the benchmarking of AI-related silicon, and present a new standard for all-round performance evaluation of hardware platforms capable of running machine and deep learning models. While Machine Learning is already a mature field, for many years it was lacking a professional, accurate and lightweight tool for measuring AI performance of various hardware used for training and inference with ML algorithms. AI Benchmark for Windows, Linux and macOS: Let the AI Games Begin.

Mac cpu speed tests