AI Benchmark app analytics for July 4

Neural Image Generation, 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, Computer Vision and NLP models. Among the tested solutions are Image Classification and Face Recognition methods, AI models performing neural image and text generation, neural networks used for Image / Video Super-Resolution and Photo Enhancement, 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 83 tests and 30 sections listed below: Section 1. Classification, MobileNet-V3 Section 2. Classification, Inception-V3 Section 3. Face Recognition, Swin Transformer Section 4. Classification, EfficientNet-B4 Section 5. Classification, MobileViT-V2 Sections 6/7. Parallel Model Execution, 8 x Inception-V3 Section 8. Object Tracking, YOLO-V8 Section 9. Optical Character Recognition, ViT Transformer Section 10. Semantic Segmentation, DeepLabV3+ Section 11. Parallel Segmentation, 2 x DeepLabV3+ Section 12. Semantic Segmentation, Segment Anything Section 13. Photo Deblurring, IMDN Section 14. Image Super-Resolution, ESRGAN Section 15. Image Super-Resolution, SRGAN Section 16. Image Denoising, U-Net Section 17. Depth Estimation, MV3-Depth Section 18. Depth Estimation, MiDaS 3.1 Section 19/20. Image Enhancement, DPED Section 21. Learned Camera ISP, MicroISP Section 22. Bokeh Effect Rendering, PyNET-V2 Mobile Section 23. FullHD Video Super-Resolution, XLSR Section 24/25. 4K Video Super-Resolution, VideoSR Section 26. Question Answering, MobileBERT Section 27. Neural Text Generation, Llama2 Section 28. Neural Text Generation, GPT2 Section 29. Neural Image Generation, Stable Diffusion V1.5 Section 30. 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, MediaTek Dimensity / Helio, Google Tensor, HiSilicon Kirin, Samsung Exynos, 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" / "Arm NN", OpenGL ES-3.0+ is required).