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TensorFlow Lite Android Object Image Segmentation Demo

Overview

This is a modification of the Tensorflow lite Object Detection Android demo to infer from the Deeplab semantic image segmentation model.

This is a camera app that continuously segments the objects into 21 classes, in the frames seen by your device's back camera, using a quantized DeepLab segmentation model. These instructions walk you through building and running the demo on an Android device.

The model files are downloaded via Gradle scripts when you build and run. You don't need to do any steps to download TFLite models into the project explicitly.

Application can run either on device or emulator. Precompiled APK is also available in the repository.

Build the demo using Android Studio

Prerequisites

  • If you don't have already, install Android Studio, following the instructions on the website.

  • You need an Android device and Android development environment with minimum API 21.

  • Android Studio 3.2 or later.

Building

  • Open Android Studio, and from the Welcome screen, select Open an existing Android Studio project.

  • From the Open File or Project window that appears, navigate to and select the tensorflow-lite/examples/object_detection/android directory from wherever you cloned the TensorFlow Lite sample GitHub repo. Click OK.

  • If it asks you to do a Gradle Sync, click OK.

  • You may also need to install various platforms and tools, if you get errors like "Failed to find target with hash string 'android-21'" and similar. Click the Run button (the green arrow) or select Run > Run 'android' from the top menu. You may need to rebuild the project using Build > Rebuild Project.

  • If it asks you to use Instant Run, click Proceed Without Instant Run.

  • Also, you need to have an Android device plugged in with developer options enabled at this point. See here for more details on setting up developer devices.

Model used

Downloading, extraction and placing it in assets folder has been managed automatically by download.gradle.

If you explicitly want to download the model, you can download from here.

Additional Note

Please do not delete the assets folder content. If you explicitly deleted the files, then please choose Build->Rebuild from menu to re-download the deleted model files into assets folder.

About

Tensorflow Lite Android Deeplab Object Image Segmentation Demo

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13 stars

Watchers

1 watching

Forks

3 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 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" + '

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TensorFlow Lite Android Object Image Segmentation Demo
Overview
This is a modification of the Tensorflow lite Object Detection Android demo to infer from the Deeplab semantic image segmentation model.
This is a camera app that continuously segments the objects into 21 classes, in the frames seen by your device's back camera, using a quantized DeepLab segmentation model. These instructions walk you through building and running the demo on an Android device.
The model files are downloaded via Gradle scripts when you build and run. You don't need to do any steps to download TFLite models into the project explicitly.
Application can run either on device or emulator. Precompiled APK is also available in the repository.
Build the demo using Android Studio
Prerequisites
  • If you don't have already, install Android Studio, following the instructions on the website.
  • You need an Android device and Android development environment with minimum API 21.
  • Android Studio 3.2 or later.
Building
  • Open Android Studio, and from the Welcome screen, select Open an existing Android Studio project.
  • From the Open File or Project window that appears, navigate to and select the tensorflow-lite/examples/object_detection/android directory from wherever you cloned the TensorFlow Lite sample GitHub repo. Click OK.
  • If it asks you to do a Gradle Sync, click OK.
  • You may also need to install various platforms and tools, if you get errors like "Failed to find target with hash string 'android-21'" and similar.
Click the Run button (the green arrow) or select Run > Run 'android' from the top menu. You may need to rebuild the project using Build > Rebuild Project.
  • If it asks you to use Instant Run, click Proceed Without Instant Run.
  • Also, you need to have an Android device plugged in with developer options enabled at this point. See here for more details on setting up developer devices.
Model used
Downloading, extraction and placing it in assets folder has been managed automatically by download.gradle.
If you explicitly want to download the model, you can download from here.
Additional Note
Please do not delete the assets folder content. If you explicitly deleted the files, then please choose Build->Rebuild from menu to re-download the deleted model files into assets folder.
About
Tensorflow Lite Android Deeplab Object Image Segmentation Demo
Resources
Readme
Activity
Stars
13 stars
Watchers
1 watching
Forks
3 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
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TensorFlow Lite Android Object Image Segmentation Demo
Overview
This is a modification of the Tensorflow lite Object Detection Android demo to infer from the Deeplab semantic image segmentation model.
This is a camera app that continuously segments the objects into 21 classes, in the frames seen by your device's back camera, using a quantized DeepLab segmentation model. These instructions walk you through building and running the demo on an Android device.
The model files are downloaded via Gradle scripts when you build and run. You don't need to do any steps to download TFLite models into the project explicitly.
Application can run either on device or emulator. Precompiled APK is also available in the repository.
Build the demo using Android Studio
Prerequisites
  • If you don't have already, install Android Studio, following the instructions on the website.
  • You need an Android device and Android development environment with minimum API 21.
  • Android Studio 3.2 or later.
Building
  • Open Android Studio, and from the Welcome screen, select Open an existing Android Studio project.
  • From the Open File or Project window that appears, navigate to and select the tensorflow-lite/examples/object_detection/android directory from wherever you cloned the TensorFlow Lite sample GitHub repo. Click OK.
  • If it asks you to do a Gradle Sync, click OK.
  • You may also need to install various platforms and tools, if you get errors like "Failed to find target with hash string 'android-21'" and similar.
Click the Run button (the green arrow) or select Run > Run 'android' from the top menu. You may need to rebuild the project using Build > Rebuild Project.
  • If it asks you to use Instant Run, click Proceed Without Instant Run.
  • Also, you need to have an Android device plugged in with developer options enabled at this point. See here for more details on setting up developer devices.
Model used
Downloading, extraction and placing it in assets folder has been managed automatically by download.gradle.
If you explicitly want to download the model, you can download from here.
Additional Note
Please do not delete the assets folder content. If you explicitly deleted the files, then please choose Build->Rebuild from menu to re-download the deleted model files into assets folder.
About
Tensorflow Lite Android Deeplab Object Image Segmentation Demo
Resources
Readme
Activity
Stars
13 stars
Watchers
1 watching
Forks
3 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 > 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
TensorFlow Lite Android Object Image Segmentation Demo
Overview
This is a modification of the Tensorflow lite Object Detection Android demo to infer from the Deeplab semantic image segmentation model.
This is a camera app that continuously segments the objects into 21 classes, in the frames seen by your device's back camera, using a quantized DeepLab segmentation model. These instructions walk you through building and running the demo on an Android device.
The model files are downloaded via Gradle scripts when you build and run. You don't need to do any steps to download TFLite models into the project explicitly.
Application can run either on device or emulator. Precompiled APK is also available in the repository.
Build the demo using Android Studio
Prerequisites
  • If you don't have already, install Android Studio, following the instructions on the website.
  • You need an Android device and Android development environment with minimum API 21.
  • Android Studio 3.2 or later.
Building
  • Open Android Studio, and from the Welcome screen, select Open an existing Android Studio project.
  • From the Open File or Project window that appears, navigate to and select the tensorflow-lite/examples/object_detection/android directory from wherever you cloned the TensorFlow Lite sample GitHub repo. Click OK.
  • If it asks you to do a Gradle Sync, click OK.
  • You may also need to install various platforms and tools, if you get errors like "Failed to find target with hash string 'android-21'" and similar.
Click the Run button (the green arrow) or select Run > Run 'android' from the top menu. You may need to rebuild the project using Build > Rebuild Project.
  • If it asks you to use Instant Run, click Proceed Without Instant Run.
  • Also, you need to have an Android device plugged in with developer options enabled at this point. See here for more details on setting up developer devices.
Model used
Downloading, extraction and placing it in assets folder has been managed automatically by download.gradle.
If you explicitly want to download the model, you can download from here.
Additional Note
Please do not delete the assets folder content. If you explicitly deleted the files, then please choose Build->Rebuild from menu to re-download the deleted model files into assets folder.
About
Tensorflow Lite Android Deeplab Object Image Segmentation Demo
Resources
Readme
Activity
Stars
13 stars
Watchers
1 watching
Forks
3 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
TensorFlow Lite Android Object Image Segmentation Demo
Overview
This is a modification of the Tensorflow lite Object Detection Android demo to infer from the Deeplab semantic image segmentation model.
This is a camera app that continuously segments the objects into 21 classes, in the frames seen by your device's back camera, using a quantized DeepLab segmentation model. These instructions walk you through building and running the demo on an Android device.
The model files are downloaded via Gradle scripts when you build and run. You don't need to do any steps to download TFLite models into the project explicitly.
Application can run either on device or emulator. Precompiled APK is also available in the repository.
Build the demo using Android Studio
Prerequisites
  • If you don't have already, install Android Studio, following the instructions on the website.
  • You need an Android device and Android development environment with minimum API 21.
  • Android Studio 3.2 or later.
Building
  • Open Android Studio, and from the Welcome screen, select Open an existing Android Studio project.
  • From the Open File or Project window that appears, navigate to and select the tensorflow-lite/examples/object_detection/android directory from wherever you cloned the TensorFlow Lite sample GitHub repo. Click OK.
  • If it asks you to do a Gradle Sync, click OK.
  • You may also need to install various platforms and tools, if you get errors like "Failed to find target with hash string 'android-21'" and similar.
Click the Run button (the green arrow) or select Run > Run 'android' from the top menu. You may need to rebuild the project using Build > Rebuild Project.
  • If it asks you to use Instant Run, click Proceed Without Instant Run.
  • Also, you need to have an Android device plugged in with developer options enabled at this point. See here for more details on setting up developer devices.
Model used
Downloading, extraction and placing it in assets folder has been managed automatically by download.gradle.
If you explicitly want to download the model, you can download from here.
Additional Note
Please do not delete the assets folder content. If you explicitly deleted the files, then please choose Build->Rebuild from menu to re-download the deleted model files into assets folder.
About
Tensorflow Lite Android Deeplab Object Image Segmentation Demo
Resources
Readme
Activity
Stars
13 stars
Watchers
1 watching
Forks
3 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
TensorFlow Lite Android Object Image Segmentation Demo
Overview
This is a modification of the Tensorflow lite Object Detection Android demo to infer from the Deeplab semantic image segmentation model.
This is a camera app that continuously segments the objects into 21 classes, in the frames seen by your device's back camera, using a quantized DeepLab segmentation model. These instructions walk you through building and running the demo on an Android device.
The model files are downloaded via Gradle scripts when you build and run. You don't need to do any steps to download TFLite models into the project explicitly.
Application can run either on device or emulator. Precompiled APK is also available in the repository.
Build the demo using Android Studio
Prerequisites
  • If you don't have already, install Android Studio, following the instructions on the website.
  • You need an Android device and Android development environment with minimum API 21.
  • Android Studio 3.2 or later.
Building
  • Open Android Studio, and from the Welcome screen, select Open an existing Android Studio project.
  • From the Open File or Project window that appears, navigate to and select the tensorflow-lite/examples/object_detection/android directory from wherever you cloned the TensorFlow Lite sample GitHub repo. Click OK.
  • If it asks you to do a Gradle Sync, click OK.
  • You may also need to install various platforms and tools, if you get errors like "Failed to find target with hash string 'android-21'" and similar.
Click the Run button (the green arrow) or select Run > Run 'android' from the top menu. You may need to rebuild the project using Build > Rebuild Project.
  • If it asks you to use Instant Run, click Proceed Without Instant Run.
  • Also, you need to have an Android device plugged in with developer options enabled at this point. See here for more details on setting up developer devices.
Model used
Downloading, extraction and placing it in assets folder has been managed automatically by download.gradle.
If you explicitly want to download the model, you can download from here.
Additional Note
Please do not delete the assets folder content. If you explicitly deleted the files, then please choose Build->Rebuild from menu to re-download the deleted model files into assets folder.
About
Tensorflow Lite Android Deeplab Object Image Segmentation Demo
Resources
Readme
Activity
Stars
13 stars
Watchers
1 watching
Forks
3 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
TensorFlow Lite Android Object Image Segmentation Demo
Overview
This is a modification of the Tensorflow lite Object Detection Android demo to infer from the Deeplab semantic image segmentation model.
This is a camera app that continuously segments the objects into 21 classes, in the frames seen by your device's back camera, using a quantized DeepLab segmentation model. These instructions walk you through building and running the demo on an Android device.
The model files are downloaded via Gradle scripts when you build and run. You don't need to do any steps to download TFLite models into the project explicitly.
Application can run either on device or emulator. Precompiled APK is also available in the repository.
Build the demo using Android Studio
Prerequisites
  • If you don't have already, install Android Studio, following the instructions on the website.
  • You need an Android device and Android development environment with minimum API 21.
  • Android Studio 3.2 or later.
Building
  • Open Android Studio, and from the Welcome screen, select Open an existing Android Studio project.
  • From the Open File or Project window that appears, navigate to and select the tensorflow-lite/examples/object_detection/android directory from wherever you cloned the TensorFlow Lite sample GitHub repo. Click OK.
  • If it asks you to do a Gradle Sync, click OK.
  • You may also need to install various platforms and tools, if you get errors like "Failed to find target with hash string 'android-21'" and similar.
Click the Run button (the green arrow) or select Run > Run 'android' from the top menu. You may need to rebuild the project using Build > Rebuild Project.
  • If it asks you to use Instant Run, click Proceed Without Instant Run.
  • Also, you need to have an Android device plugged in with developer options enabled at this point. See here for more details on setting up developer devices.
Model used
Downloading, extraction and placing it in assets folder has been managed automatically by download.gradle.
If you explicitly want to download the model, you can download from here.
Additional Note
Please do not delete the assets folder content. If you explicitly deleted the files, then please choose Build->Rebuild from menu to re-download the deleted model files into assets folder.
About
Tensorflow Lite Android Deeplab Object Image Segmentation Demo
Resources
Readme
Activity
Stars
13 stars
Watchers
1 watching
Forks
3 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
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TensorFlow Lite Android Object Image Segmentation Demo
Overview
This is a modification of the Tensorflow lite Object Detection Android demo to infer from the Deeplab semantic image segmentation model.
This is a camera app that continuously segments the objects into 21 classes, in the frames seen by your device's back camera, using a quantized DeepLab segmentation model. These instructions walk you through building and running the demo on an Android device.
The model files are downloaded via Gradle scripts when you build and run. You don't need to do any steps to download TFLite models into the project explicitly.
Application can run either on device or emulator. Precompiled APK is also available in the repository.
Build the demo using Android Studio
Prerequisites
  • If you don't have already, install Android Studio, following the instructions on the website.
  • You need an Android device and Android development environment with minimum API 21.
  • Android Studio 3.2 or later.
Building
  • Open Android Studio, and from the Welcome screen, select Open an existing Android Studio project.
  • From the Open File or Project window that appears, navigate to and select the tensorflow-lite/examples/object_detection/android directory from wherever you cloned the TensorFlow Lite sample GitHub repo. Click OK.
  • If it asks you to do a Gradle Sync, click OK.
  • You may also need to install various platforms and tools, if you get errors like "Failed to find target with hash string 'android-21'" and similar.
Click the Run button (the green arrow) or select Run > Run 'android' from the top menu. You may need to rebuild the project using Build > Rebuild Project.
  • If it asks you to use Instant Run, click Proceed Without Instant Run.
  • Also, you need to have an Android device plugged in with developer options enabled at this point. See here for more details on setting up developer devices.
Model used
Downloading, extraction and placing it in assets folder has been managed automatically by download.gradle.
If you explicitly want to download the model, you can download from here.
Additional Note
Please do not delete the assets folder content. If you explicitly deleted the files, then please choose Build->Rebuild from menu to re-download the deleted model files into assets folder.
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Tensorflow Lite Android Deeplab Object Image Segmentation Demo
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