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Machine-Learning: Real-Time-Object-Classification

CML

Application of machine learning models to recognise objects from the CIFAR-10 dataset. This work uses the TensorFlow framework to build, train, and deploy models to produce real-time object classifications.

Note

Realtime Preview

realtie-preview

Visualisations

cnn_conv2d_fmcnn_conv2d_fm
Batch Normalised Model Confusion MatrixBatch Normalised Model Inference Timings with 128 batch size

TensorFlow Setup

This project utilises the TensorFlow framework to create and train machine learning models. Please ensure that the TensorFlow is properly installed on your system. Please refer to the TensorFlow documentation.

Important

This work is compatible to run on both CPU and GPU. To improve performance, it is advised to use the GPU for training models. Please ensure that the GPU drivers are up to date and the necessary development toolkits are installed on your system. For NVIDIA GPUs, please install the CUDA Toolkit and cuDNN SDK.

Getting Started

To run the application, run the following commands.

Linux / WSL2

Create a Python virtual environment

python -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
python RealtimeClassification.py

About

Real-time Object Classification using TensorFlow and OpenCV

Topics

machine-learningopencvpythontensorflow

Resources

Readme
MIT license
Activity

Stars

0 stars

Watchers

1 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 
 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
Machine-Learning: Real-Time-Object-Classification
CML
Application of machine learning models to recognise objects from the CIFAR-10 dataset. This work uses the TensorFlow framework to build, train, and deploy models to produce real-time object classifications.
Note
Realtime Preview
realtie-preview
Visualisations
cnn_conv2d_fmcnn_conv2d_fm
Batch Normalised Model Confusion MatrixBatch Normalised Model Inference Timings with 128 batch size
TensorFlow Setup
This project utilises the TensorFlow framework to create and train machine learning models. Please ensure that the TensorFlow is properly installed on your system. Please refer to the TensorFlow documentation.
Important
This work is compatible to run on both CPU and GPU. To improve performance, it is advised to use the GPU for training models. Please ensure that the GPU drivers are up to date and the necessary development toolkits are installed on your system. For NVIDIA GPUs, please install the CUDA Toolkit and cuDNN SDK.
Getting Started
To run the application, run the following commands.
Linux / WSL2
Create a Python virtual environment
python -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
python RealtimeClassification.py
About
Real-time Object Classification using TensorFlow and OpenCV
Topics
machine-learningopencvpythontensorflow
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
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, '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
Machine-Learning: Real-Time-Object-Classification
CML
Application of machine learning models to recognise objects from the CIFAR-10 dataset. This work uses the TensorFlow framework to build, train, and deploy models to produce real-time object classifications.
Note
Realtime Preview
realtie-preview
Visualisations
cnn_conv2d_fmcnn_conv2d_fm
Batch Normalised Model Confusion MatrixBatch Normalised Model Inference Timings with 128 batch size
TensorFlow Setup
This project utilises the TensorFlow framework to create and train machine learning models. Please ensure that the TensorFlow is properly installed on your system. Please refer to the TensorFlow documentation.
Important
This work is compatible to run on both CPU and GPU. To improve performance, it is advised to use the GPU for training models. Please ensure that the GPU drivers are up to date and the necessary development toolkits are installed on your system. For NVIDIA GPUs, please install the CUDA Toolkit and cuDNN SDK.
Getting Started
To run the application, run the following commands.
Linux / WSL2
Create a Python virtual environment
python -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
python RealtimeClassification.py
About
Real-time Object Classification using TensorFlow and OpenCV
Topics
machine-learningopencvpythontensorflow
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
1 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 > 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
Machine-Learning: Real-Time-Object-Classification
CML
Application of machine learning models to recognise objects from the CIFAR-10 dataset. This work uses the TensorFlow framework to build, train, and deploy models to produce real-time object classifications.
Note
Realtime Preview
realtie-preview
Visualisations
cnn_conv2d_fmcnn_conv2d_fm
Batch Normalised Model Confusion MatrixBatch Normalised Model Inference Timings with 128 batch size
TensorFlow Setup
This project utilises the TensorFlow framework to create and train machine learning models. Please ensure that the TensorFlow is properly installed on your system. Please refer to the TensorFlow documentation.
Important
This work is compatible to run on both CPU and GPU. To improve performance, it is advised to use the GPU for training models. Please ensure that the GPU drivers are up to date and the necessary development toolkits are installed on your system. For NVIDIA GPUs, please install the CUDA Toolkit and cuDNN SDK.
Getting Started
To run the application, run the following commands.
Linux / WSL2
Create a Python virtual environment
python -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
python RealtimeClassification.py
About
Real-time Object Classification using TensorFlow and OpenCV
Topics
machine-learningopencvpythontensorflow
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
1 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
Machine-Learning: Real-Time-Object-Classification
CML
Application of machine learning models to recognise objects from the CIFAR-10 dataset. This work uses the TensorFlow framework to build, train, and deploy models to produce real-time object classifications.
Note
Realtime Preview
realtie-preview
Visualisations
cnn_conv2d_fmcnn_conv2d_fm
Batch Normalised Model Confusion MatrixBatch Normalised Model Inference Timings with 128 batch size
TensorFlow Setup
This project utilises the TensorFlow framework to create and train machine learning models. Please ensure that the TensorFlow is properly installed on your system. Please refer to the TensorFlow documentation.
Important
This work is compatible to run on both CPU and GPU. To improve performance, it is advised to use the GPU for training models. Please ensure that the GPU drivers are up to date and the necessary development toolkits are installed on your system. For NVIDIA GPUs, please install the CUDA Toolkit and cuDNN SDK.
Getting Started
To run the application, run the following commands.
Linux / WSL2
Create a Python virtual environment
python -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
python RealtimeClassification.py
About
Real-time Object Classification using TensorFlow and OpenCV
Topics
machine-learningopencvpythontensorflow
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
1 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
Machine-Learning: Real-Time-Object-Classification
CML
Application of machine learning models to recognise objects from the CIFAR-10 dataset. This work uses the TensorFlow framework to build, train, and deploy models to produce real-time object classifications.
Note
Realtime Preview
realtie-preview
Visualisations
cnn_conv2d_fmcnn_conv2d_fm
Batch Normalised Model Confusion MatrixBatch Normalised Model Inference Timings with 128 batch size
TensorFlow Setup
This project utilises the TensorFlow framework to create and train machine learning models. Please ensure that the TensorFlow is properly installed on your system. Please refer to the TensorFlow documentation.
Important
This work is compatible to run on both CPU and GPU. To improve performance, it is advised to use the GPU for training models. Please ensure that the GPU drivers are up to date and the necessary development toolkits are installed on your system. For NVIDIA GPUs, please install the CUDA Toolkit and cuDNN SDK.
Getting Started
To run the application, run the following commands.
Linux / WSL2
Create a Python virtual environment
python -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
python RealtimeClassification.py
About
Real-time Object Classification using TensorFlow and OpenCV
Topics
machine-learningopencvpythontensorflow
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
1 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
Machine-Learning: Real-Time-Object-Classification
CML
Application of machine learning models to recognise objects from the CIFAR-10 dataset. This work uses the TensorFlow framework to build, train, and deploy models to produce real-time object classifications.
Note
Realtime Preview
realtie-preview
Visualisations
cnn_conv2d_fmcnn_conv2d_fm
Batch Normalised Model Confusion MatrixBatch Normalised Model Inference Timings with 128 batch size
TensorFlow Setup
This project utilises the TensorFlow framework to create and train machine learning models. Please ensure that the TensorFlow is properly installed on your system. Please refer to the TensorFlow documentation.
Important
This work is compatible to run on both CPU and GPU. To improve performance, it is advised to use the GPU for training models. Please ensure that the GPU drivers are up to date and the necessary development toolkits are installed on your system. For NVIDIA GPUs, please install the CUDA Toolkit and cuDNN SDK.
Getting Started
To run the application, run the following commands.
Linux / WSL2
Create a Python virtual environment
python -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
python RealtimeClassification.py
About
Real-time Object Classification using TensorFlow and OpenCV
Topics
machine-learningopencvpythontensorflow
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
1 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
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Machine-Learning: Real-Time-Object-Classification
CML
Application of machine learning models to recognise objects from the CIFAR-10 dataset. This work uses the TensorFlow framework to build, train, and deploy models to produce real-time object classifications.
Note
Realtime Preview
realtie-preview
Visualisations
cnn_conv2d_fmcnn_conv2d_fm
Batch Normalised Model Confusion MatrixBatch Normalised Model Inference Timings with 128 batch size
TensorFlow Setup
This project utilises the TensorFlow framework to create and train machine learning models. Please ensure that the TensorFlow is properly installed on your system. Please refer to the TensorFlow documentation.
Important
This work is compatible to run on both CPU and GPU. To improve performance, it is advised to use the GPU for training models. Please ensure that the GPU drivers are up to date and the necessary development toolkits are installed on your system. For NVIDIA GPUs, please install the CUDA Toolkit and cuDNN SDK.
Getting Started
To run the application, run the following commands.
Linux / WSL2
Create a Python virtual environment
python -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
python RealtimeClassification.py
About
Real-time Object Classification using TensorFlow and OpenCV
Topics
machine-learningopencvpythontensorflow
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages