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Análisis de la app MLPerf Mobile para 1 de marzo

MLPerf Mobile

MLPerf Mobile

  • MLCommons
  • Google Play Store
  • Gratis
  • Herramientas
MLPerf Mobile is a free, open-source benchmarking tool designed to measure the performance of mobile devices like smartphones and tablets in various artificial intelligence (AI) and machine learning (ML) tasks. Workloads tested include image classification, language understanding, super resolution upscaling, and image generation based on text prompts. This benchmark makes use of hardware AI acceleration on many of the latest mobile devices to ensure the best performance where possible. MLPerf Mobile is built and maintained by the MLPerf Mobile working group at MLCommons®, a non-profit AI/ML engineering consortium made up of 125+ members including industry firms and academics from a host of different institutions around the globe. MLCommons produces world-class benchmarks for AI training and inference across many system scales, from large data center installations to tiny embedded devices. Features of MLPerf Mobile include: - Benchmark tests across various domains based on state-of-the-art AI models, including: - Image classification - Object detection - Image segmentation - Language understanding - Super resolution - Image generation from text prompts - Custom-tuned AI acceleration on the latest mobile devices and SoCs. - Broad support for Android devices via TensorFlow Lite delegate fallback acceleration. - Tailored test modes for everyone from casual users wanting a quick performance assessment to MLCommons members intending to submit official results for publication. - Customizable cool-down delays between tests to avoid thermal throttling and ensure accurate results. - Optional cloud-based results storage so you can save and access your past results from multiple devices in one place. (This feature is free of charge but requires account registration.) MLPerf Mobile is typically updated multiple times each year with new tests and acceleration support as AI models and mobile hardware capabilities evolve. Please note that some benchmark tests may not be supported, and therefore may not show up as available for testing, on older devices. Source code and documentation for the MLPerf Mobile app are available on the MLCommons Github repo. For user support or questions, please feel free to open issues in the app’s Github repo: github.com/mlcommons/mobile_app_open If you or your organization is interested in becoming an MLCommons member, please contact participation@mlcommons.org for more information.
MLPerf Mobile

Clasificación del uso de MLPerf Mobile

El rango de uso se basa en el algoritmo de Similarweb que calcula las instalaciones actuales y los usuarios activos durante un periodo de 28 días.

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Usuarios activos diarios

Analiza los patrones de uso de los usuarios de MLPerf Mobile viendo las descargas de MLPerf Mobile y los usuarios activos diarios a lo largo del tiempo.

Usuarios

Analiza los patrones de uso de los usuarios de MLPerf Mobile viendo las descargas de MLPerf Mobile y los usuarios activos diarios a lo largo del tiempo.

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enefebmar

Estadísticas de clasificación de MLPerf Mobile a lo largo del tiempo

Rango de uso de Similarweb & Rango de Google Play Store para MLPerf Mobile

Clasificación

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Clasificación por país MLPerf Mobile

Países en los que MLPerf Mobile tiene la clasificación más alta en sus categorías principales

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Intereses de los usuarios & Top categorias

Principales categorías y aplicaciones utilizadas por los usuarios de MLPerf Mobile.

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Principales competidores & aplicaciones alternativas

Aplicaciones con una alta probabilidad de ser utilizadas por los mismos usuarios, desde la misma tienda.

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MLPerf Mobile VS.

marzo 1, 2026