AI Benchmark
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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).
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).
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Bewertungen und Rezensionen
4.4
1.45 Tsg. Rezensionen
Neuigkeiten
1. Updated Qualcomm QNN and MediaTek Neuron delegates.
2. Enhanced stability and accuracy of the power consumption test.
3. Various bug fixes and performance improvements.
2. Enhanced stability and accuracy of the power consumption test.
3. Various bug fixes and performance improvements.
