{"id":2687,"date":"2021-05-11T11:34:02","date_gmt":"2021-05-11T01:34:02","guid":{"rendered":"https:\/\/wordpress.temp.irap-dev.org\/omdena-challenge-collaborators-share-results\/"},"modified":"2026-04-30T12:38:10","modified_gmt":"2026-04-30T02:38:10","slug":"omdena-challenge-collaborators-share-results","status":"publish","type":"post","link":"https:\/\/irap.org\/es\/omdena-challenge-collaborators-share-results\/","title":{"rendered":"Los colaboradores del Desaf\u00edo Omdena comparten resultados"},"content":{"rendered":"

Colaboradores en el Reto de 8 semanas de Omdena iRAP<\/a> Las organizaciones que buscaban obtener soluciones basadas en IA mediante colaboraci\u00f3n colectiva para mapear el riesgo de accidentes y generar datos de atributos de calificaci\u00f3n de estrellas iRAP han compartido algunos de los resultados de sus publicaciones.<\/p>\n

A principios de este a\u00f1o, 50 expertos en IA y aprendizaje autom\u00e1tico, entre los que se incluyen estudiantes con aspiraciones, cient\u00edficos de datos, ingenieros de aprendizaje autom\u00e1tico y datos, y expertos en la materia de todo el mundo, trabajaron con miembros del Equipo de Innovaci\u00f3n y Desarrollo iRAP, centrado en encontrar una soluci\u00f3n para la recopilaci\u00f3n y codificaci\u00f3n acelerada e inteligente de datos de atributos viales (AiRAP) para prevenir accidentes de tr\u00e1fico y salvar vidas.<\/p>\n

Lea estos m\u00e9todos de Python perspicaces y bien aplicados:<\/p>\n

\"Vehicle-Count-iRAP-1080x675\"<\/p>\n

Utilizando redes neuronales convolucionales para mejorar la seguridad vial y salvar vidas\u201d por\u00a0Lois Anne Leal<\/a>\u00a0y\u00a0Chaitree Baradkar<\/a> \u2013 https:\/\/bit.ly\/3gK8men<\/a><\/p>\n

\u201cAn\u00e1lisis de caracter\u00edsticas y aprendizaje autom\u00e1tico para la seguridad vial\u201d por\u00a0Le\u00f3n Hamnett<\/a>
https:\/\/bit.ly\/3gJGuqt<\/a><\/p>\n

\"Feature\"\"bend13-678x675<\/p>\n

\u201cDetecci\u00f3n de objetos en im\u00e1genes 2D para localizar autom\u00f3viles y peatones\u201d por\u00a0Giulio Giaconi<\/a>\u00a0y\u00a0Dave Bunten<\/a>
https:\/\/bit.ly\/3vce0cV<\/a><\/p>\n

\u201cAn\u00e1lisis exploratorio de datos avanzado de seguridad vial del Reino Unido mediante Python\u201d por\u00a0Nabanita Roy<\/a>
https:\/\/bit.ly\/3dY8l4v<\/a><\/p>\n

\"eda20\"stream1\"<\/p>\n

\u201cStreamlit 101: Implementaci\u00f3n de un panel de control de AutoML Streamlit para predecir y mostrar resultados\u201d por\u00a0MUHAMMAD ALI<\/a>
https:\/\/bit.ly\/3vov9QP<\/a><\/p>\n

\u201cAprendiendo OpenCV desde cero para construir un detector de peatones\u201d por\u00a0Rutuja Kawade<\/a>
https:\/\/bit.ly\/2PsvPoS<\/a><\/p>\n

\"opencv7\"<\/p>\n

iRAP agradece sinceramente al equipo de Omdena. Los resultados servir\u00e1n de base para importantes avances en #3starorbetter, carreteras para todos los usuarios que salvar\u00e1n vidas.\u00a0<\/p>","protected":false},"excerpt":{"rendered":"

Collaborators on the Omdena iRAP 8 Week Challenge which aimed to crowd source AI-based solutions to map crash risk and generate iRAP star rating attribute data have shared some of their publication results. Earlier this year, 50 AI and machine learning experts including aspiring students, data scientists, ML and data engineers, and domain experts from […]<\/p>\n","protected":false},"author":10,"featured_media":2688,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[28,266],"tags":[147,343,344,148],"class_list":["post-2687","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-global-news","category-airap","tag-airap","tag-challenge","tag-innovation","tag-omdena"],"acf":[],"yoast_head":"\nOmdena Challenge collaborators share results - iRAP<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/irap.org\/es\/omdena-challenge-collaborators-share-results\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Omdena Challenge collaborators share results\" \/>\n<meta property=\"og:description\" content=\"Collaborators on the Omdena iRAP 8 Week Challenge which aimed to crowd source AI-based solutions to map crash risk and generate iRAP star rating attribute data have shared some of their publication results. 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