AI Benchmark

Face Recognition, Image Classification, Question Answering...

Is your smartphone capable of running the latest Deep Neural Networks to perform these and many other AI-based tasks? Does it have a dedicated AI Chip? Is it fast enough? Run AI Benchmark to professionally evaluate its AI Performance!

Current phone ranking: http://ai-benchmark.com/ranking

AI Benchmark measures the speed, accuracy, power consumption and memory requirements for several key AI and Computer Vision algorithms. Among the tested solutions are Image Classification and Face Recognition methods, Neural Networks used for Image / Video Super-Resolution and Photo Enhancement, AI models predicting text and performing question answering, as well as AI solutions used in autonomous driving systems and smartphones for real-time Depth Estimation and Semantic Image Segmentation. The visualization of the algorithms’ outputs allows to assess their results graphically and to get to know the current state-of-the-art in various AI fields.

In total, AI Benchmark consists of 78 tests and 26 sections listed below:

Section 1. Classification, MobileNet-V2

Section 2. Classification, Inception-V3

Section 3. Face Recognition, MobileNet-V3

Section 4. Classification, EfficientNet-B4

Sections 5/6. Parallel Model Execution, 8 x Inception-V3

Section 7. Object Tracking, YOLO-V4

Section 8. Optical Character Recognition, CRNN

Section 9. Semantic Segmentation, DeepLabV3+

Section 10. Parallel Segmentation, 2 x DeepLabV3+

Section 11. Photo Deblurring, IMDN

Section 12. Image Super-Resolution, ESRGAN

Section 13. Image Super-Resolution, SRGAN

Section 14. Image Denoising, U-Net

Section 15. Depth Estimation, MV3-Depth

Section 16. Image Enhancement, DPED ResNet

Section 17. Image Enhancement, DPED Instance

Section 18. Bokeh Effect Rendering, PyNET+

Section 19. Learned Camera ISP, PUNET

Section 20. FullHD Video Super-Resolution, XLSR

Section 21/22. 4K Video Super-Resolution, VideoSR

Section 23. Text Completion, LSTM

Section 24. Question Answering, MobileBERT

Section 25. Text Completion, ALBERT

Section 26. Memory Limits, ResNet

Besides that, one can load and test their own TensorFlow Lite deep learning models in the PRO Mode.

A detailed description of the tests can be found here: http://ai-benchmark.com/tests.html

Note: Hardware acceleration is supported on all mobile SoCs with dedicated NPUs and AI accelerators, including Qualcomm Snapdragon, HiSilicon Kirin, Samsung Exynos , MediaTek Helio / Dimensity and UNISOC Tiger chipsets. Starting from AI Benchmark v4, one can also enable GPU-based AI acceleration on older devices in the settings ("Accelerate" -> "Enable GPU Acceleration", OpenGL ES-3.0+ is required).