From owner-svn-ports-head@freebsd.org Sat Jul 20 22:13:56 2019 Return-Path: Delivered-To: svn-ports-head@mailman.nyi.freebsd.org Received: from mx1.freebsd.org (mx1.freebsd.org [IPv6:2610:1c1:1:606c::19:1]) by mailman.nyi.freebsd.org (Postfix) with ESMTP id C2447A18F8; Sat, 20 Jul 2019 22:13:56 +0000 (UTC) (envelope-from yuri@FreeBSD.org) Received: from mxrelay.nyi.freebsd.org (mxrelay.nyi.freebsd.org [IPv6:2610:1c1:1:606c::19:3]) (using TLSv1.3 with cipher TLS_AES_256_GCM_SHA384 (256/256 bits) server-signature RSA-PSS (4096 bits) client-signature RSA-PSS (4096 bits) client-digest SHA256) (Client CN "mxrelay.nyi.freebsd.org", Issuer "Let's Encrypt Authority X3" (verified OK)) by mx1.freebsd.org (Postfix) with ESMTPS id A6B7C91AFE; Sat, 20 Jul 2019 22:13:56 +0000 (UTC) (envelope-from yuri@FreeBSD.org) Received: from repo.freebsd.org (repo.freebsd.org [IPv6:2610:1c1:1:6068::e6a:0]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (Client did not present a certificate) by mxrelay.nyi.freebsd.org (Postfix) with ESMTPS id 9B3B21925E; Sat, 20 Jul 2019 22:13:56 +0000 (UTC) (envelope-from yuri@FreeBSD.org) Received: from repo.freebsd.org ([127.0.1.37]) by repo.freebsd.org (8.15.2/8.15.2) with ESMTP id x6KMDuM5058675; Sat, 20 Jul 2019 22:13:56 GMT (envelope-from yuri@FreeBSD.org) Received: (from yuri@localhost) by repo.freebsd.org (8.15.2/8.15.2/Submit) id x6KMDuTE058674; Sat, 20 Jul 2019 22:13:56 GMT (envelope-from yuri@FreeBSD.org) Message-Id: <201907202213.x6KMDuTE058674@repo.freebsd.org> X-Authentication-Warning: repo.freebsd.org: yuri set sender to yuri@FreeBSD.org using -f From: Yuri Victorovich Date: Sat, 20 Jul 2019 22:13:56 +0000 (UTC) To: ports-committers@freebsd.org, svn-ports-all@freebsd.org, svn-ports-head@freebsd.org Subject: svn commit: r507026 - head/science/ncnn X-SVN-Group: ports-head X-SVN-Commit-Author: yuri X-SVN-Commit-Paths: head/science/ncnn X-SVN-Commit-Revision: 507026 X-SVN-Commit-Repository: ports MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit X-Rspamd-Queue-Id: A6B7C91AFE X-Spamd-Bar: -- Authentication-Results: mx1.freebsd.org X-Spamd-Result: default: False [-2.96 / 15.00]; local_wl_from(0.00)[FreeBSD.org]; NEURAL_HAM_MEDIUM(-1.00)[-1.000,0]; NEURAL_HAM_SHORT(-0.96)[-0.956,0]; NEURAL_HAM_LONG(-1.00)[-1.000,0]; ASN(0.00)[asn:11403, ipnet:2610:1c1:1::/48, country:US] X-BeenThere: svn-ports-head@freebsd.org X-Mailman-Version: 2.1.29 Precedence: list List-Id: SVN commit messages for the ports tree for head List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Sat, 20 Jul 2019 22:13:56 -0000 Author: yuri Date: Sat Jul 20 22:13:56 2019 New Revision: 507026 URL: https://svnweb.freebsd.org/changeset/ports/507026 Log: science/ncnn: Fix grammar and spelling in pkg-descr Modified: head/science/ncnn/pkg-descr Modified: head/science/ncnn/pkg-descr ============================================================================== --- head/science/ncnn/pkg-descr Sat Jul 20 22:01:17 2019 (r507025) +++ head/science/ncnn/pkg-descr Sat Jul 20 22:13:56 2019 (r507026) @@ -1,11 +1,11 @@ ncnn is a high-performance neural network inference computing framework -optimized for mobile platforms. ncnn is deeply considerate about deployment and -uses on mobile phones from the beginning of design. ncnn does not have third -party dependencies. it is cross-platform, and runs faster than all known open -source frameworks on mobile phone cpu. Developers can easily deploy deep -learning algorithm models to the mobile platform by using efficient ncnn -implementation, create intelligent APPs, and bring the artificial intelligence -to your fingertips. ncnn is currently being used in many Tencent applications, -such as QQ, Qzone, WeChat, Pitu and so on. +optimized for mobile platforms. ncnn is deeply concerned about its deployment +and use on mobile phones from the beginning of its design. ncnn does not have +third party dependencies. It is cross-platform, and runs faster than all known +open-source frameworks on mobile phone CPUs. Developers can easily deploy deep +learning algorithm models to mobile platforms by using the efficient ncnn +implementation. They can create intelligent apps, and bring the artificial +intelligence to your fingertips. ncnn is currently being used in many Tencent +applications, such as QQ, Qzone, WeChat, Pitu and so on. WWW: https://github.com/Tencent/ncnn