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CNN-Image-Classification

This is to create a repos for CNN image classifications model feasbility investigation.

1st is to build a CNN model to predict cat or dog and data augmentation is used to improved the model. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/c/dogs-vs-cats

2nd one is build a CNN model to predict fashion labels using Fashion MNIST dataset. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/zalando-research/fashionmnist

3rd one is to build a LE-Net model to predict traffic sign images (43 classes). The model is implemented as referred in original publication with model evaluation and model prediction.

4th one is to fine-tune ResNet and InceptionV3 models for covid and Pneumonia detections (4 classes in total). The model is created based on transfer learning with the last 10 layer trainable. Grid search is used to optimized the training parameters, including dropout rate, batch size, epoch. Dataset for training is too large to share, but happy to share on demand.

Production and deployment level codes are to be opened soon. Stay tuned.

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GitHub - luke4u/Image-Classification: This is to create a repos for image classifications. · GitHub
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CNN-Image-Classification

This is to create a repos for CNN image classifications model feasbility investigation.

1st is to build a CNN model to predict cat or dog and data augmentation is used to improved the model. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/c/dogs-vs-cats

2nd one is build a CNN model to predict fashion labels using Fashion MNIST dataset. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/zalando-research/fashionmnist

3rd one is to build a LE-Net model to predict traffic sign images (43 classes). The model is implemented as referred in original publication with model evaluation and model prediction.

4th one is to fine-tune ResNet and InceptionV3 models for covid and Pneumonia detections (4 classes in total). The model is created based on transfer learning with the last 10 layer trainable. Grid search is used to optimized the training parameters, including dropout rate, batch size, epoch. Dataset for training is too large to share, but happy to share on demand.

Production and deployment level codes are to be opened soon. Stay tuned.

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This is to create a repos for image classifications.

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Force GitHub README to respect dark mode (function() { var style = document.createElement('style'); style.textContent = ' .markdown-body { color-scheme: dark light; } .markdown-body pre { background: #161b22 !important; } .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; } .markdown-body table th, .markdown-body table td { border-color: #30363d !important; } .markdown-body img { background: #0d1117; } .markdown-body blockquote { border-left-color: #8b949e; } .markdown-body hr { border-color: #30363d; } '; document.head.appendChild(style); })(); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - luke4u/Image-Classification: This is to create a repos for image classifications. · GitHub
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CNN-Image-Classification

This is to create a repos for CNN image classifications model feasbility investigation.

1st is to build a CNN model to predict cat or dog and data augmentation is used to improved the model. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/c/dogs-vs-cats

2nd one is build a CNN model to predict fashion labels using Fashion MNIST dataset. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/zalando-research/fashionmnist

3rd one is to build a LE-Net model to predict traffic sign images (43 classes). The model is implemented as referred in original publication with model evaluation and model prediction.

4th one is to fine-tune ResNet and InceptionV3 models for covid and Pneumonia detections (4 classes in total). The model is created based on transfer learning with the last 10 layer trainable. Grid search is used to optimized the training parameters, including dropout rate, batch size, epoch. Dataset for training is too large to share, but happy to share on demand.

Production and deployment level codes are to be opened soon. Stay tuned.

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This is to create a repos for image classifications.

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Highlight search terms from Google/DuckDuckGo/Bing referrer (function() { var ref = document.referrer; var terms = []; if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) { var url = new URL(ref); var q = url.searchParams.get('q') || url.searchParams.get('p'); if (q) { terms = q.split(/\s+/).filter(function(t) { return t.length > 2; }); } } if (terms.length === 0) return; var style = document.createElement('style'); style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }'; document.head.appendChild(style); function highlight(node) { if (node.nodeType === 3) { // text node var text = node.textContent; var found = false; terms.forEach(function(term) { var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi'); if (regex.test(text)) { found = true; var frag = document.createDocumentFragment(); var parts = text.split(regex); parts.forEach(function(part, i) { if (i % 2 === 0) { frag.appendChild(document.createTextNode(part)); } else { var span = document.createElement('span'); span.className = 'userscript-highlight'; span.textContent = part; frag.appendChild(span); } }); node.parentNode.replaceChild(frag, node); } }); } else if (node.nodeType === 1 && node.childNodes) { // element var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT']; if (!skipTags.includes(node.tagName)) { Array.from(node.childNodes).forEach(highlight); } } } highlight(document.body); // Re-highlight on dynamic content var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1 || node.nodeType === 3) highlight(node); }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - luke4u/Image-Classification: This is to create a repos for image classifications. · GitHub
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CNN-Image-Classification

This is to create a repos for CNN image classifications model feasbility investigation.

1st is to build a CNN model to predict cat or dog and data augmentation is used to improved the model. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/c/dogs-vs-cats

2nd one is build a CNN model to predict fashion labels using Fashion MNIST dataset. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/zalando-research/fashionmnist

3rd one is to build a LE-Net model to predict traffic sign images (43 classes). The model is implemented as referred in original publication with model evaluation and model prediction.

4th one is to fine-tune ResNet and InceptionV3 models for covid and Pneumonia detections (4 classes in total). The model is created based on transfer learning with the last 10 layer trainable. Grid search is used to optimized the training parameters, including dropout rate, batch size, epoch. Dataset for training is too large to share, but happy to share on demand.

Production and deployment level codes are to be opened soon. Stay tuned.

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This is to create a repos for image classifications.

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2 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Strip utm_, fbclid, gclid, etc. from all links on page (function() { var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content', 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid', 'ref', 'ref_src', 'source', 'medium', 'campaign']; function cleanUrl(url) { try { var u = new URL(url, window.location.origin); var changed = false; trackingParams.forEach(function(p) { if (u.searchParams.has(p)) { u.searchParams.delete(p); changed = true; } }); return changed ? u.toString() : url; } catch (e) { return url; } } function cleanLinks() { document.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } cleanLinks(); var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1) { if (node.tagName === 'A') cleanLinks(); node.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + ' GitHub - luke4u/Image-Classification: This is to create a repos for image classifications. · GitHub
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CNN-Image-Classification

This is to create a repos for CNN image classifications model feasbility investigation.

1st is to build a CNN model to predict cat or dog and data augmentation is used to improved the model. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/c/dogs-vs-cats

2nd one is build a CNN model to predict fashion labels using Fashion MNIST dataset. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/zalando-research/fashionmnist

3rd one is to build a LE-Net model to predict traffic sign images (43 classes). The model is implemented as referred in original publication with model evaluation and model prediction.

4th one is to fine-tune ResNet and InceptionV3 models for covid and Pneumonia detections (4 classes in total). The model is created based on transfer learning with the last 10 layer trainable. Grid search is used to optimized the training parameters, including dropout rate, batch size, epoch. Dataset for training is too large to share, but happy to share on demand.

Production and deployment level codes are to be opened soon. Stay tuned.

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This is to create a repos for image classifications.

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4 stars

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2 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Auto-enable theater mode on YouTube (function() { function tryTheater() { var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]'); if (btn && !btn.classList.contains('activated')) { btn.click(); } } // Try immediately tryTheater(); // Try after navigation (SPA) var lastUrl = location.href; setInterval(function() { if (location.href !== lastUrl) { lastUrl = location.href; setTimeout(tryTheater, 500); } }, 1000); // Also try on player load var observer = new MutationObserver(tryTheater); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - luke4u/Image-Classification: This is to create a repos for image classifications. · GitHub
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CNN-Image-Classification

This is to create a repos for CNN image classifications model feasbility investigation.

1st is to build a CNN model to predict cat or dog and data augmentation is used to improved the model. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/c/dogs-vs-cats

2nd one is build a CNN model to predict fashion labels using Fashion MNIST dataset. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/zalando-research/fashionmnist

3rd one is to build a LE-Net model to predict traffic sign images (43 classes). The model is implemented as referred in original publication with model evaluation and model prediction.

4th one is to fine-tune ResNet and InceptionV3 models for covid and Pneumonia detections (4 classes in total). The model is created based on transfer learning with the last 10 layer trainable. Grid search is used to optimized the training parameters, including dropout rate, batch size, epoch. Dataset for training is too large to share, but happy to share on demand.

Production and deployment level codes are to be opened soon. Stay tuned.

About

This is to create a repos for image classifications.

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Resources

Stars

4 stars

Watchers

2 watching

Forks

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Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Remove or un-stick sticky/fixed headers that block content (function() { function unstick() { document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) { if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') { el.style.position = 'static'; el.style.top = 'auto'; el.style.zIndex = 'auto'; } }); } unstick(); var observer = new MutationObserver(unstick); observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] }); })(); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - luke4u/Image-Classification: This is to create a repos for image classifications. · GitHub
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CNN-Image-Classification

This is to create a repos for CNN image classifications model feasbility investigation.

1st is to build a CNN model to predict cat or dog and data augmentation is used to improved the model. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/c/dogs-vs-cats

2nd one is build a CNN model to predict fashion labels using Fashion MNIST dataset. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/zalando-research/fashionmnist

3rd one is to build a LE-Net model to predict traffic sign images (43 classes). The model is implemented as referred in original publication with model evaluation and model prediction.

4th one is to fine-tune ResNet and InceptionV3 models for covid and Pneumonia detections (4 classes in total). The model is created based on transfer learning with the last 10 layer trainable. Grid search is used to optimized the training parameters, including dropout rate, batch size, epoch. Dataset for training is too large to share, but happy to share on demand.

Production and deployment level codes are to be opened soon. Stay tuned.

About

This is to create a repos for image classifications.

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Resources

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4 stars

Watchers

2 watching

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Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Universal Dark Mode - works on any site (function() { var enabled = true; function applyDarkMode() { if (!enabled) return; // Create style element if it doesn't exist var style = document.getElementById('universal-dark-mode-style'); if (!style) { style = document.createElement('style'); style.id = 'universal-dark-mode-style'; document.head.appendChild(style); } // Dark mode CSS - inverts colors but preserves images/video style.textContent = ' /* Invert everything except media */ html { filter: invert(1) hue-rotate(180deg) !important; background: #1a1a2e !important; } /* Restore images, videos, iframes, canvas */ img, video, iframe, canvas, svg, picture, [style*="background-image"] { filter: invert(1) hue-rotate(180deg) !important; } /* Preserve specific elements that should not be inverted */ .no-dark-mode, .no-dark-mode *, [data-theme="light"], [data-theme="light"], .ace_editor, .ace_editor *, .CodeMirror, .CodeMirror *, .monaco-editor, .monaco-editor *, .markdown-body pre, .markdown-body pre *, .highlight, .highlight *, pre code, pre code * { filter: none !important; } /* Fix common UI elements */ .modal, .popup, .dropdown-menu, .tooltip, .popover { filter: invert(1) hue-rotate(180deg) !important; background: #2d2d44 !important; border-color: #444 !important; } /* Scrollbars */ ::-webkit-scrollbar { background: #1a1a2e !important; } ::-webkit-scrollbar-thumb { background: #444 !important; } ::-webkit-scrollbar-thumb:hover { background: #555 !important; } /* Selection */ ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; } ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; } '; } function removeDarkMode() { var style = document.getElementById('universal-dark-mode-style'); if (style) style.remove(); } // Toggle with Alt+Shift+D document.addEventListener('keydown', function(e) { if (e.altKey && e.shiftKey && e.key === 'D') { e.preventDefault(); enabled = !enabled; if (enabled) { applyDarkMode(); console.log('[Universal Dark Mode] Enabled'); } else { removeDarkMode(); console.log('[Universal Dark Mode] Disabled'); } } }); // Apply on load applyDarkMode(); // Re-apply on dynamic content var observer = new MutationObserver(function(mutations) { if (enabled && !document.getElementById('universal-dark-mode-style')) { applyDarkMode(); } }); observer.observe(document.head, { childList: true }); console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle'); })(); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })(); GitHub - luke4u/Image-Classification: This is to create a repos for image classifications. · GitHub
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CNN-Image-Classification

This is to create a repos for CNN image classifications model feasbility investigation.

1st is to build a CNN model to predict cat or dog and data augmentation is used to improved the model. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/c/dogs-vs-cats

2nd one is build a CNN model to predict fashion labels using Fashion MNIST dataset. Due to size limit, unable to upload train and test files Please refer to Kaggle for data source: https://www.kaggle.com/zalando-research/fashionmnist

3rd one is to build a LE-Net model to predict traffic sign images (43 classes). The model is implemented as referred in original publication with model evaluation and model prediction.

4th one is to fine-tune ResNet and InceptionV3 models for covid and Pneumonia detections (4 classes in total). The model is created based on transfer learning with the last 10 layer trainable. Grid search is used to optimized the training parameters, including dropout rate, batch size, epoch. Dataset for training is too large to share, but happy to share on demand.

Production and deployment level codes are to be opened soon. Stay tuned.

About

This is to create a repos for image classifications.

Topics

Resources

Stars

4 stars

Watchers

2 watching

Forks

Releases

Packages

Contributors

Languages