BranchesTags

Repository files navigation

NNStreamer

GitterCode CoverageCoverity Scan Defect StatusDailyBuildGitHub repo sizeGitHub issuesGitHub pull requestsCII Best Practices

Neural Network Support as Gstreamer Plugins.

NNStreamer is a set of Gstreamer plugins that allow Gstreamer developers to adopt neural network models easily and efficiently and neural network developers to manage neural network pipelines and their filters easily and efficiently.

Architectural Description (WIP)

NNStreamer: Stream Processing Paradigm for Neural Networks ... [pdf/tech report]
GStreamer Conference 2018, NNStreamer [media] [pdf/slides]
Naver Tech Talk (Korean), 2018 [media] [pdf/slides]
Samsung Developer Conference 2019, NNStreamer [media]
ResearchGate Page of NNStreamer

Official Releases

TizenUbuntuAndroid/NDK BuildAndroid/APKYoctomacOS
5.5M2 and later16.04/18.04/20.049/P9/PZeus and later
armarmv7l badgeAvailableReadyAvailableReadyN/A
arm64aarch64 badgeAvailableReadyandroid badgePlannedN/A
x64x64 badgeubuntu badgeReadyReadyReadyAvailable
x86x86 badgeN/AN/AN/AN/AN/A
PublishTizen RepoPPAJCenterBrew Tap
APIC/C# (Official)CJavaCC
  • Ready: CI system ensures build-ability and unit-testing. Users may easily build and execute. However, we do not have automated release & deployment system for this instance.
  • Available: binary packages are released and deployed automatically and periodically along with CI tests.
  • Daily Release
  • SDK Support: Tizen Studio (5.5 M2+) / Android Studio (JCenter, "nnstreamer")
  • Enabled features of official releases

Objectives

  • Provide neural network framework connectivities (e.g., tensorflow, caffe) for gstreamer streams.

    • Efficient Streaming for AI Projects: Apply efficient and flexible stream pipeline to neural networks.
    • Intelligent Media Filters!: Use a neural network model as a media filter / converter.
    • Composite Models!: Multiple neural network models in a single stream pipeline instance.
    • Multi Modal Intelligence!: Multiple sources and stream paths for neural network models.

  • Provide easy methods to construct media streams with neural network models using the de-facto-standard media stream framework, GStreamer.

    • Gstreamer users: use neural network models as if they are yet another media filters.
    • Neural network developers: manage media streams easily and efficiently.

Maintainers

Committers

Components

Note that this project has just started and many of the components are in design phase. In Component Description page, we describe nnstreamer components of the following three categories: data type definitions, gstreamer elements (plugins), and other misc components.

Getting Started

For more details, please access the following manuals.

  • For Linux-like systems such as Tizen, Debian, and Ubuntu, press here.
  • For macOS systems, press here.
  • To build an API library for Android, press here.

Applications

CI Server

AI Acceleration Hardware Support

Although a framework may accelerate transparently as Tensorflow-GPU does, nnstreamer provides various hardware acceleration subplugins.

  • Movidius-X via ncsdk2 subplugin: Released
  • Movidius-X via openVINO subplugin: Released
  • Edge-TPU via edgetpu subplugin: Released
  • ONE runtime via nnfw(an old name of ONE) subplugin: Released
  • ARMNN via armnn subplugin: Released
  • Verisilicon-Vivante via vivante subplugin: Released
  • Qualcomm SNPE via snpe subplugin: Released
  • Exynos NPU: WIP

Contributing

Contributions are welcome! Please see our Contributing Guide for more details.

About

🔀 Neural Network (NN) Streamer for AI Projects.

Resources

Readme
LGPL-2.1 license

Code of conduct

Code of conduct

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all \u003cpre\u003e\u003ccode\u003e blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks"); } } catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); } })(); (function(){ try { var __m = "github.com"; var __re = new RegExp('^' + "github\\.com" + '
Skip to content
BranchesTags

Repository files navigation

NNStreamer

GitterCode CoverageCoverity Scan Defect StatusDailyBuildGitHub repo sizeGitHub issuesGitHub pull requestsCII Best Practices

Neural Network Support as Gstreamer Plugins.

NNStreamer is a set of Gstreamer plugins that allow Gstreamer developers to adopt neural network models easily and efficiently and neural network developers to manage neural network pipelines and their filters easily and efficiently.

Architectural Description (WIP)

NNStreamer: Stream Processing Paradigm for Neural Networks ... [pdf/tech report]
GStreamer Conference 2018, NNStreamer [media] [pdf/slides]
Naver Tech Talk (Korean), 2018 [media] [pdf/slides]
Samsung Developer Conference 2019, NNStreamer [media]
ResearchGate Page of NNStreamer

Official Releases

TizenUbuntuAndroid/NDK BuildAndroid/APKYoctomacOS
5.5M2 and later16.04/18.04/20.049/P9/PZeus and later
armarmv7l badgeAvailableReadyAvailableReadyN/A
arm64aarch64 badgeAvailableReadyandroid badgePlannedN/A
x64x64 badgeubuntu badgeReadyReadyReadyAvailable
x86x86 badgeN/AN/AN/AN/AN/A
PublishTizen RepoPPAJCenterBrew Tap
APIC/C# (Official)CJavaCC
  • Ready: CI system ensures build-ability and unit-testing. Users may easily build and execute. However, we do not have automated release & deployment system for this instance.
  • Available: binary packages are released and deployed automatically and periodically along with CI tests.
  • Daily Release
  • SDK Support: Tizen Studio (5.5 M2+) / Android Studio (JCenter, "nnstreamer")
  • Enabled features of official releases

Objectives

  • Provide neural network framework connectivities (e.g., tensorflow, caffe) for gstreamer streams.

    • Efficient Streaming for AI Projects: Apply efficient and flexible stream pipeline to neural networks.
    • Intelligent Media Filters!: Use a neural network model as a media filter / converter.
    • Composite Models!: Multiple neural network models in a single stream pipeline instance.
    • Multi Modal Intelligence!: Multiple sources and stream paths for neural network models.

  • Provide easy methods to construct media streams with neural network models using the de-facto-standard media stream framework, GStreamer.

    • Gstreamer users: use neural network models as if they are yet another media filters.
    • Neural network developers: manage media streams easily and efficiently.

Maintainers

Committers

Components

Note that this project has just started and many of the components are in design phase. In Component Description page, we describe nnstreamer components of the following three categories: data type definitions, gstreamer elements (plugins), and other misc components.

Getting Started

For more details, please access the following manuals.

  • For Linux-like systems such as Tizen, Debian, and Ubuntu, press here.
  • For macOS systems, press here.
  • To build an API library for Android, press here.

Applications

CI Server

AI Acceleration Hardware Support

Although a framework may accelerate transparently as Tensorflow-GPU does, nnstreamer provides various hardware acceleration subplugins.

  • Movidius-X via ncsdk2 subplugin: Released
  • Movidius-X via openVINO subplugin: Released
  • Edge-TPU via edgetpu subplugin: Released
  • ONE runtime via nnfw(an old name of ONE) subplugin: Released
  • ARMNN via armnn subplugin: Released
  • Verisilicon-Vivante via vivante subplugin: Released
  • Qualcomm SNPE via snpe subplugin: Released
  • Exynos NPU: WIP

Contributing

Contributions are welcome! Please see our Contributing Guide for more details.

About

🔀 Neural Network (NN) Streamer for AI Projects.

Resources

Readme
LGPL-2.1 license

Code of conduct

Code of conduct

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content
BranchesTags

Repository files navigation

NNStreamer

GitterCode CoverageCoverity Scan Defect StatusDailyBuildGitHub repo sizeGitHub issuesGitHub pull requestsCII Best Practices

Neural Network Support as Gstreamer Plugins.

NNStreamer is a set of Gstreamer plugins that allow Gstreamer developers to adopt neural network models easily and efficiently and neural network developers to manage neural network pipelines and their filters easily and efficiently.

Architectural Description (WIP)

NNStreamer: Stream Processing Paradigm for Neural Networks ... [pdf/tech report]
GStreamer Conference 2018, NNStreamer [media] [pdf/slides]
Naver Tech Talk (Korean), 2018 [media] [pdf/slides]
Samsung Developer Conference 2019, NNStreamer [media]
ResearchGate Page of NNStreamer

Official Releases

TizenUbuntuAndroid/NDK BuildAndroid/APKYoctomacOS
5.5M2 and later16.04/18.04/20.049/P9/PZeus and later
armarmv7l badgeAvailableReadyAvailableReadyN/A
arm64aarch64 badgeAvailableReadyandroid badgePlannedN/A
x64x64 badgeubuntu badgeReadyReadyReadyAvailable
x86x86 badgeN/AN/AN/AN/AN/A
PublishTizen RepoPPAJCenterBrew Tap
APIC/C# (Official)CJavaCC
  • Ready: CI system ensures build-ability and unit-testing. Users may easily build and execute. However, we do not have automated release & deployment system for this instance.
  • Available: binary packages are released and deployed automatically and periodically along with CI tests.
  • Daily Release
  • SDK Support: Tizen Studio (5.5 M2+) / Android Studio (JCenter, "nnstreamer")
  • Enabled features of official releases

Objectives

  • Provide neural network framework connectivities (e.g., tensorflow, caffe) for gstreamer streams.

    • Efficient Streaming for AI Projects: Apply efficient and flexible stream pipeline to neural networks.
    • Intelligent Media Filters!: Use a neural network model as a media filter / converter.
    • Composite Models!: Multiple neural network models in a single stream pipeline instance.
    • Multi Modal Intelligence!: Multiple sources and stream paths for neural network models.

  • Provide easy methods to construct media streams with neural network models using the de-facto-standard media stream framework, GStreamer.

    • Gstreamer users: use neural network models as if they are yet another media filters.
    • Neural network developers: manage media streams easily and efficiently.

Maintainers

Committers

Components

Note that this project has just started and many of the components are in design phase. In Component Description page, we describe nnstreamer components of the following three categories: data type definitions, gstreamer elements (plugins), and other misc components.

Getting Started

For more details, please access the following manuals.

  • For Linux-like systems such as Tizen, Debian, and Ubuntu, press here.
  • For macOS systems, press here.
  • To build an API library for Android, press here.

Applications

CI Server

AI Acceleration Hardware Support

Although a framework may accelerate transparently as Tensorflow-GPU does, nnstreamer provides various hardware acceleration subplugins.

  • Movidius-X via ncsdk2 subplugin: Released
  • Movidius-X via openVINO subplugin: Released
  • Edge-TPU via edgetpu subplugin: Released
  • ONE runtime via nnfw(an old name of ONE) subplugin: Released
  • ARMNN via armnn subplugin: Released
  • Verisilicon-Vivante via vivante subplugin: Released
  • Qualcomm SNPE via snpe subplugin: Released
  • Exynos NPU: WIP

Contributing

Contributions are welcome! Please see our Contributing Guide for more details.

About

🔀 Neural Network (NN) Streamer for AI Projects.

Resources

Readme
LGPL-2.1 license

Code of conduct

Code of conduct

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length \u003e 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content
BranchesTags

Repository files navigation

NNStreamer

GitterCode CoverageCoverity Scan Defect StatusDailyBuildGitHub repo sizeGitHub issuesGitHub pull requestsCII Best Practices

Neural Network Support as Gstreamer Plugins.

NNStreamer is a set of Gstreamer plugins that allow Gstreamer developers to adopt neural network models easily and efficiently and neural network developers to manage neural network pipelines and their filters easily and efficiently.

Architectural Description (WIP)

NNStreamer: Stream Processing Paradigm for Neural Networks ... [pdf/tech report]
GStreamer Conference 2018, NNStreamer [media] [pdf/slides]
Naver Tech Talk (Korean), 2018 [media] [pdf/slides]
Samsung Developer Conference 2019, NNStreamer [media]
ResearchGate Page of NNStreamer

Official Releases

TizenUbuntuAndroid/NDK BuildAndroid/APKYoctomacOS
5.5M2 and later16.04/18.04/20.049/P9/PZeus and later
armarmv7l badgeAvailableReadyAvailableReadyN/A
arm64aarch64 badgeAvailableReadyandroid badgePlannedN/A
x64x64 badgeubuntu badgeReadyReadyReadyAvailable
x86x86 badgeN/AN/AN/AN/AN/A
PublishTizen RepoPPAJCenterBrew Tap
APIC/C# (Official)CJavaCC
  • Ready: CI system ensures build-ability and unit-testing. Users may easily build and execute. However, we do not have automated release & deployment system for this instance.
  • Available: binary packages are released and deployed automatically and periodically along with CI tests.
  • Daily Release
  • SDK Support: Tizen Studio (5.5 M2+) / Android Studio (JCenter, "nnstreamer")
  • Enabled features of official releases

Objectives

  • Provide neural network framework connectivities (e.g., tensorflow, caffe) for gstreamer streams.

    • Efficient Streaming for AI Projects: Apply efficient and flexible stream pipeline to neural networks.
    • Intelligent Media Filters!: Use a neural network model as a media filter / converter.
    • Composite Models!: Multiple neural network models in a single stream pipeline instance.
    • Multi Modal Intelligence!: Multiple sources and stream paths for neural network models.

  • Provide easy methods to construct media streams with neural network models using the de-facto-standard media stream framework, GStreamer.

    • Gstreamer users: use neural network models as if they are yet another media filters.
    • Neural network developers: manage media streams easily and efficiently.

Maintainers

Committers

Components

Note that this project has just started and many of the components are in design phase. In Component Description page, we describe nnstreamer components of the following three categories: data type definitions, gstreamer elements (plugins), and other misc components.

Getting Started

For more details, please access the following manuals.

  • For Linux-like systems such as Tizen, Debian, and Ubuntu, press here.
  • For macOS systems, press here.
  • To build an API library for Android, press here.

Applications

CI Server

AI Acceleration Hardware Support

Although a framework may accelerate transparently as Tensorflow-GPU does, nnstreamer provides various hardware acceleration subplugins.

  • Movidius-X via ncsdk2 subplugin: Released
  • Movidius-X via openVINO subplugin: Released
  • Edge-TPU via edgetpu subplugin: Released
  • ONE runtime via nnfw(an old name of ONE) subplugin: Released
  • ARMNN via armnn subplugin: Released
  • Verisilicon-Vivante via vivante subplugin: Released
  • Qualcomm SNPE via snpe subplugin: Released
  • Exynos NPU: WIP

Contributing

Contributions are welcome! Please see our Contributing Guide for more details.

About

🔀 Neural Network (NN) Streamer for AI Projects.

Resources

Readme
LGPL-2.1 license

Code of conduct

Code of conduct

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
Skip to content
BranchesTags

Repository files navigation

NNStreamer

GitterCode CoverageCoverity Scan Defect StatusDailyBuildGitHub repo sizeGitHub issuesGitHub pull requestsCII Best Practices

Neural Network Support as Gstreamer Plugins.

NNStreamer is a set of Gstreamer plugins that allow Gstreamer developers to adopt neural network models easily and efficiently and neural network developers to manage neural network pipelines and their filters easily and efficiently.

Architectural Description (WIP)

NNStreamer: Stream Processing Paradigm for Neural Networks ... [pdf/tech report]
GStreamer Conference 2018, NNStreamer [media] [pdf/slides]
Naver Tech Talk (Korean), 2018 [media] [pdf/slides]
Samsung Developer Conference 2019, NNStreamer [media]
ResearchGate Page of NNStreamer

Official Releases

TizenUbuntuAndroid/NDK BuildAndroid/APKYoctomacOS
5.5M2 and later16.04/18.04/20.049/P9/PZeus and later
armarmv7l badgeAvailableReadyAvailableReadyN/A
arm64aarch64 badgeAvailableReadyandroid badgePlannedN/A
x64x64 badgeubuntu badgeReadyReadyReadyAvailable
x86x86 badgeN/AN/AN/AN/AN/A
PublishTizen RepoPPAJCenterBrew Tap
APIC/C# (Official)CJavaCC
  • Ready: CI system ensures build-ability and unit-testing. Users may easily build and execute. However, we do not have automated release & deployment system for this instance.
  • Available: binary packages are released and deployed automatically and periodically along with CI tests.
  • Daily Release
  • SDK Support: Tizen Studio (5.5 M2+) / Android Studio (JCenter, "nnstreamer")
  • Enabled features of official releases

Objectives

  • Provide neural network framework connectivities (e.g., tensorflow, caffe) for gstreamer streams.

    • Efficient Streaming for AI Projects: Apply efficient and flexible stream pipeline to neural networks.
    • Intelligent Media Filters!: Use a neural network model as a media filter / converter.
    • Composite Models!: Multiple neural network models in a single stream pipeline instance.
    • Multi Modal Intelligence!: Multiple sources and stream paths for neural network models.

  • Provide easy methods to construct media streams with neural network models using the de-facto-standard media stream framework, GStreamer.

    • Gstreamer users: use neural network models as if they are yet another media filters.
    • Neural network developers: manage media streams easily and efficiently.

Maintainers

Committers

Components

Note that this project has just started and many of the components are in design phase. In Component Description page, we describe nnstreamer components of the following three categories: data type definitions, gstreamer elements (plugins), and other misc components.

Getting Started

For more details, please access the following manuals.

  • For Linux-like systems such as Tizen, Debian, and Ubuntu, press here.
  • For macOS systems, press here.
  • To build an API library for Android, press here.

Applications

CI Server

AI Acceleration Hardware Support

Although a framework may accelerate transparently as Tensorflow-GPU does, nnstreamer provides various hardware acceleration subplugins.

  • Movidius-X via ncsdk2 subplugin: Released
  • Movidius-X via openVINO subplugin: Released
  • Edge-TPU via edgetpu subplugin: Released
  • ONE runtime via nnfw(an old name of ONE) subplugin: Released
  • ARMNN via armnn subplugin: Released
  • Verisilicon-Vivante via vivante subplugin: Released
  • Qualcomm SNPE via snpe subplugin: Released
  • Exynos NPU: WIP

Contributing

Contributions are welcome! Please see our Contributing Guide for more details.

About

🔀 Neural Network (NN) Streamer for AI Projects.

Resources

Readme
LGPL-2.1 license

Code of conduct

Code of conduct

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content
BranchesTags

Repository files navigation

NNStreamer

GitterCode CoverageCoverity Scan Defect StatusDailyBuildGitHub repo sizeGitHub issuesGitHub pull requestsCII Best Practices

Neural Network Support as Gstreamer Plugins.

NNStreamer is a set of Gstreamer plugins that allow Gstreamer developers to adopt neural network models easily and efficiently and neural network developers to manage neural network pipelines and their filters easily and efficiently.

Architectural Description (WIP)

NNStreamer: Stream Processing Paradigm for Neural Networks ... [pdf/tech report]
GStreamer Conference 2018, NNStreamer [media] [pdf/slides]
Naver Tech Talk (Korean), 2018 [media] [pdf/slides]
Samsung Developer Conference 2019, NNStreamer [media]
ResearchGate Page of NNStreamer

Official Releases

TizenUbuntuAndroid/NDK BuildAndroid/APKYoctomacOS
5.5M2 and later16.04/18.04/20.049/P9/PZeus and later
armarmv7l badgeAvailableReadyAvailableReadyN/A
arm64aarch64 badgeAvailableReadyandroid badgePlannedN/A
x64x64 badgeubuntu badgeReadyReadyReadyAvailable
x86x86 badgeN/AN/AN/AN/AN/A
PublishTizen RepoPPAJCenterBrew Tap
APIC/C# (Official)CJavaCC
  • Ready: CI system ensures build-ability and unit-testing. Users may easily build and execute. However, we do not have automated release & deployment system for this instance.
  • Available: binary packages are released and deployed automatically and periodically along with CI tests.
  • Daily Release
  • SDK Support: Tizen Studio (5.5 M2+) / Android Studio (JCenter, "nnstreamer")
  • Enabled features of official releases

Objectives

  • Provide neural network framework connectivities (e.g., tensorflow, caffe) for gstreamer streams.

    • Efficient Streaming for AI Projects: Apply efficient and flexible stream pipeline to neural networks.
    • Intelligent Media Filters!: Use a neural network model as a media filter / converter.
    • Composite Models!: Multiple neural network models in a single stream pipeline instance.
    • Multi Modal Intelligence!: Multiple sources and stream paths for neural network models.

  • Provide easy methods to construct media streams with neural network models using the de-facto-standard media stream framework, GStreamer.

    • Gstreamer users: use neural network models as if they are yet another media filters.
    • Neural network developers: manage media streams easily and efficiently.

Maintainers

Committers

Components

Note that this project has just started and many of the components are in design phase. In Component Description page, we describe nnstreamer components of the following three categories: data type definitions, gstreamer elements (plugins), and other misc components.

Getting Started

For more details, please access the following manuals.

  • For Linux-like systems such as Tizen, Debian, and Ubuntu, press here.
  • For macOS systems, press here.
  • To build an API library for Android, press here.

Applications

CI Server

AI Acceleration Hardware Support

Although a framework may accelerate transparently as Tensorflow-GPU does, nnstreamer provides various hardware acceleration subplugins.

  • Movidius-X via ncsdk2 subplugin: Released
  • Movidius-X via openVINO subplugin: Released
  • Edge-TPU via edgetpu subplugin: Released
  • ONE runtime via nnfw(an old name of ONE) subplugin: Released
  • ARMNN via armnn subplugin: Released
  • Verisilicon-Vivante via vivante subplugin: Released
  • Qualcomm SNPE via snpe subplugin: Released
  • Exynos NPU: WIP

Contributing

Contributions are welcome! Please see our Contributing Guide for more details.

About

🔀 Neural Network (NN) Streamer for AI Projects.

Resources

Readme
LGPL-2.1 license

Code of conduct

Code of conduct

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content
BranchesTags

Repository files navigation

NNStreamer

GitterCode CoverageCoverity Scan Defect StatusDailyBuildGitHub repo sizeGitHub issuesGitHub pull requestsCII Best Practices

Neural Network Support as Gstreamer Plugins.

NNStreamer is a set of Gstreamer plugins that allow Gstreamer developers to adopt neural network models easily and efficiently and neural network developers to manage neural network pipelines and their filters easily and efficiently.

Architectural Description (WIP)

NNStreamer: Stream Processing Paradigm for Neural Networks ... [pdf/tech report]
GStreamer Conference 2018, NNStreamer [media] [pdf/slides]
Naver Tech Talk (Korean), 2018 [media] [pdf/slides]
Samsung Developer Conference 2019, NNStreamer [media]
ResearchGate Page of NNStreamer

Official Releases

TizenUbuntuAndroid/NDK BuildAndroid/APKYoctomacOS
5.5M2 and later16.04/18.04/20.049/P9/PZeus and later
armarmv7l badgeAvailableReadyAvailableReadyN/A
arm64aarch64 badgeAvailableReadyandroid badgePlannedN/A
x64x64 badgeubuntu badgeReadyReadyReadyAvailable
x86x86 badgeN/AN/AN/AN/AN/A
PublishTizen RepoPPAJCenterBrew Tap
APIC/C# (Official)CJavaCC
  • Ready: CI system ensures build-ability and unit-testing. Users may easily build and execute. However, we do not have automated release & deployment system for this instance.
  • Available: binary packages are released and deployed automatically and periodically along with CI tests.
  • Daily Release
  • SDK Support: Tizen Studio (5.5 M2+) / Android Studio (JCenter, "nnstreamer")
  • Enabled features of official releases

Objectives

  • Provide neural network framework connectivities (e.g., tensorflow, caffe) for gstreamer streams.

    • Efficient Streaming for AI Projects: Apply efficient and flexible stream pipeline to neural networks.
    • Intelligent Media Filters!: Use a neural network model as a media filter / converter.
    • Composite Models!: Multiple neural network models in a single stream pipeline instance.
    • Multi Modal Intelligence!: Multiple sources and stream paths for neural network models.

  • Provide easy methods to construct media streams with neural network models using the de-facto-standard media stream framework, GStreamer.

    • Gstreamer users: use neural network models as if they are yet another media filters.
    • Neural network developers: manage media streams easily and efficiently.

Maintainers

Committers

Components

Note that this project has just started and many of the components are in design phase. In Component Description page, we describe nnstreamer components of the following three categories: data type definitions, gstreamer elements (plugins), and other misc components.

Getting Started

For more details, please access the following manuals.

  • For Linux-like systems such as Tizen, Debian, and Ubuntu, press here.
  • For macOS systems, press here.
  • To build an API library for Android, press here.

Applications

CI Server

AI Acceleration Hardware Support

Although a framework may accelerate transparently as Tensorflow-GPU does, nnstreamer provides various hardware acceleration subplugins.

  • Movidius-X via ncsdk2 subplugin: Released
  • Movidius-X via openVINO subplugin: Released
  • Edge-TPU via edgetpu subplugin: Released
  • ONE runtime via nnfw(an old name of ONE) subplugin: Released
  • ARMNN via armnn subplugin: Released
  • Verisilicon-Vivante via vivante subplugin: Released
  • Qualcomm SNPE via snpe subplugin: Released
  • Exynos NPU: WIP

Contributing

Contributions are welcome! Please see our Contributing Guide for more details.

About

🔀 Neural Network (NN) Streamer for AI Projects.

Resources

Readme
LGPL-2.1 license

Code of conduct

Code of conduct

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
Skip to content
BranchesTags

Repository files navigation

NNStreamer

GitterCode CoverageCoverity Scan Defect StatusDailyBuildGitHub repo sizeGitHub issuesGitHub pull requestsCII Best Practices

Neural Network Support as Gstreamer Plugins.

NNStreamer is a set of Gstreamer plugins that allow Gstreamer developers to adopt neural network models easily and efficiently and neural network developers to manage neural network pipelines and their filters easily and efficiently.

Architectural Description (WIP)

NNStreamer: Stream Processing Paradigm for Neural Networks ... [pdf/tech report]
GStreamer Conference 2018, NNStreamer [media] [pdf/slides]
Naver Tech Talk (Korean), 2018 [media] [pdf/slides]
Samsung Developer Conference 2019, NNStreamer [media]
ResearchGate Page of NNStreamer

Official Releases

TizenUbuntuAndroid/NDK BuildAndroid/APKYoctomacOS
5.5M2 and later16.04/18.04/20.049/P9/PZeus and later
armarmv7l badgeAvailableReadyAvailableReadyN/A
arm64aarch64 badgeAvailableReadyandroid badgePlannedN/A
x64x64 badgeubuntu badgeReadyReadyReadyAvailable
x86x86 badgeN/AN/AN/AN/AN/A
PublishTizen RepoPPAJCenterBrew Tap
APIC/C# (Official)CJavaCC
  • Ready: CI system ensures build-ability and unit-testing. Users may easily build and execute. However, we do not have automated release & deployment system for this instance.
  • Available: binary packages are released and deployed automatically and periodically along with CI tests.
  • Daily Release
  • SDK Support: Tizen Studio (5.5 M2+) / Android Studio (JCenter, "nnstreamer")
  • Enabled features of official releases

Objectives

  • Provide neural network framework connectivities (e.g., tensorflow, caffe) for gstreamer streams.

    • Efficient Streaming for AI Projects: Apply efficient and flexible stream pipeline to neural networks.
    • Intelligent Media Filters!: Use a neural network model as a media filter / converter.
    • Composite Models!: Multiple neural network models in a single stream pipeline instance.
    • Multi Modal Intelligence!: Multiple sources and stream paths for neural network models.

  • Provide easy methods to construct media streams with neural network models using the de-facto-standard media stream framework, GStreamer.

    • Gstreamer users: use neural network models as if they are yet another media filters.
    • Neural network developers: manage media streams easily and efficiently.

Maintainers

Committers

Components

Note that this project has just started and many of the components are in design phase. In Component Description page, we describe nnstreamer components of the following three categories: data type definitions, gstreamer elements (plugins), and other misc components.

Getting Started

For more details, please access the following manuals.

  • For Linux-like systems such as Tizen, Debian, and Ubuntu, press here.
  • For macOS systems, press here.
  • To build an API library for Android, press here.

Applications

CI Server

AI Acceleration Hardware Support

Although a framework may accelerate transparently as Tensorflow-GPU does, nnstreamer provides various hardware acceleration subplugins.

  • Movidius-X via ncsdk2 subplugin: Released
  • Movidius-X via openVINO subplugin: Released
  • Edge-TPU via edgetpu subplugin: Released
  • ONE runtime via nnfw(an old name of ONE) subplugin: Released
  • ARMNN via armnn subplugin: Released
  • Verisilicon-Vivante via vivante subplugin: Released
  • Qualcomm SNPE via snpe subplugin: Released
  • Exynos NPU: WIP

Contributing

Contributions are welcome! Please see our Contributing Guide for more details.

About

🔀 Neural Network (NN) Streamer for AI Projects.

Resources

Readme
LGPL-2.1 license

Code of conduct

Code of conduct

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages