AI Benchmark
140 TRUSTED APP
v6.0.0 by Ignatov Andrey
About AI Benchmark
AI Benchmark (Package Name: org.benchmark.demo) is developed by Ignatov Andrey and the latest version of AI Benchmark 6.0.0 was updated on September 26, 2024. AI Benchmark is in the category of Tools. You can check all apps from the developer of AI Benchmark and find 162 alternative apps to AI Benchmark on Android. Currently this app is for free. This app can be downloaded on Android 5.0+ on APKPure.fo or Google Play. All APK/XAPK files on APKPure.fo are original and 100% safe with fast download.
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).
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).
AI Benchmark 6.0.0 Update
1. New tasks and models: Vision Transformer (ViT) architectures, Large Language Models (LLMs), Stable Diffusion network, etc.
2. Added tests checking the performance of quantized INT16 inference.
3. LiteRT (TFLite) runtime updated to version 2.17.
4. Updated Qualcomm QNN, MediaTek Neuron, TFLite NNAPI, GPU and Hexagon NN delegates.
5. Added Arm NN delegate for AI inference acceleration on Mali GPUs.
6. The total number of tests increased to 83.
2. Added tests checking the performance of quantized INT16 inference.
3. LiteRT (TFLite) runtime updated to version 2.17.
4. Updated Qualcomm QNN, MediaTek Neuron, TFLite NNAPI, GPU and Hexagon NN delegates.
5. Added Arm NN delegate for AI inference acceleration on Mali GPUs.
6. The total number of tests increased to 83.
Read More
Previous Versions More
AI Benchmark
6.0.0
XAPK
APKs
September 26, 2024
1.39 GB
Variant
Arch
Version
DPI
AI Benchmark
5.1.2
XAPK
APKs
March 6, 2024
778.85 MB
Variant
Arch
Version
DPI
AI Benchmark
5.1.1
XAPK
APKs
November 27, 2023
778.66 MB
Variant
Arch
Version
DPI
AI Benchmark
5.1.0
XAPK
APKs
August 8, 2023
773.01 MB
Variant
Arch
Version
DPI
More Information
Package Name:
Update Date:
2024-09-26
Latest Version:
6.0.0
Need Update:
Available on:
Requirements:
Android 5.0+
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