BranchesTags

Repository files navigation

Image-Colorization

Running the Model

  1. Download Places365 to the Dataset directory.
  2. Extract places365_train_standard.txt from places365_train_standard.zip to the Dataset directory.
  3. Start training the model by running all the blocks in UNet_PatchGan_v4.ipnyb.
  4. To evaluate the model with new pictures, modify ckpt_path and img_path in the third and last blocks of Evaluate.ipnyb respectively.

To change the root directory of datasets, modify dataset.py on Line 8 and 34. Be sure to include places365_train_standard.txt in the new directory.

Things to Note

  1. The model is mainly based on PatchGan. A classifier and Convolution Block Attention Module (CBAM) are incorporated to improve the model's performance.
  2. For this model, pictures are represented in LAB color space. There are two main reasons why we utilize LAB instead of RGB: Given that the "L" channel can be used as input, the model only needs to generate and concatenate values in the "A" and "B" channels; on the other hand, it will be easier for the model to colorize images of varying size, since the rescaled "A" and "B" channels can be concatenated using the original "L" channel with no distortion in pictures.

Project Paper

About

An Image colorization algorithm using PatchGan and Convolution Block Attention Modules (CBAM)

Topics

cbamimage-colorizationpatchgan

Resources

Readme
Activity

Stars

1 star

Watchers

1 watching

Forks

1 fork
Report repository

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
Image-Colorization
Running the Model
  1. Download Places365 to the Dataset directory.
  2. Extract places365_train_standard.txt from places365_train_standard.zip to the Dataset directory.
  3. Start training the model by running all the blocks in UNet_PatchGan_v4.ipnyb.
  4. To evaluate the model with new pictures, modify ckpt_path and img_path in the third and last blocks of Evaluate.ipnyb respectively.
To change the root directory of datasets, modify dataset.py on Line 8 and 34. Be sure to include places365_train_standard.txt in the new directory.
Things to Note
  1. The model is mainly based on PatchGan. A classifier and Convolution Block Attention Module (CBAM) are incorporated to improve the model's performance.
  2. For this model, pictures are represented in LAB color space. There are two main reasons why we utilize LAB instead of RGB: Given that the "L" channel can be used as input, the model only needs to generate and concatenate values in the "A" and "B" channels; on the other hand, it will be easier for the model to colorize images of varying size, since the rescaled "A" and "B" channels can be concatenated using the original "L" channel with no distortion in pictures.
Project Paper
About
An Image colorization algorithm using PatchGan and Convolution Block Attention Modules (CBAM)
Topics
cbamimage-colorizationpatchgan
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
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
Image-Colorization
Running the Model
  1. Download Places365 to the Dataset directory.
  2. Extract places365_train_standard.txt from places365_train_standard.zip to the Dataset directory.
  3. Start training the model by running all the blocks in UNet_PatchGan_v4.ipnyb.
  4. To evaluate the model with new pictures, modify ckpt_path and img_path in the third and last blocks of Evaluate.ipnyb respectively.
To change the root directory of datasets, modify dataset.py on Line 8 and 34. Be sure to include places365_train_standard.txt in the new directory.
Things to Note
  1. The model is mainly based on PatchGan. A classifier and Convolution Block Attention Module (CBAM) are incorporated to improve the model's performance.
  2. For this model, pictures are represented in LAB color space. There are two main reasons why we utilize LAB instead of RGB: Given that the "L" channel can be used as input, the model only needs to generate and concatenate values in the "A" and "B" channels; on the other hand, it will be easier for the model to colorize images of varying size, since the rescaled "A" and "B" channels can be concatenated using the original "L" channel with no distortion in pictures.
Project Paper
About
An Image colorization algorithm using PatchGan and Convolution Block Attention Modules (CBAM)
Topics
cbamimage-colorizationpatchgan
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
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
Image-Colorization
Running the Model
  1. Download Places365 to the Dataset directory.
  2. Extract places365_train_standard.txt from places365_train_standard.zip to the Dataset directory.
  3. Start training the model by running all the blocks in UNet_PatchGan_v4.ipnyb.
  4. To evaluate the model with new pictures, modify ckpt_path and img_path in the third and last blocks of Evaluate.ipnyb respectively.
To change the root directory of datasets, modify dataset.py on Line 8 and 34. Be sure to include places365_train_standard.txt in the new directory.
Things to Note
  1. The model is mainly based on PatchGan. A classifier and Convolution Block Attention Module (CBAM) are incorporated to improve the model's performance.
  2. For this model, pictures are represented in LAB color space. There are two main reasons why we utilize LAB instead of RGB: Given that the "L" channel can be used as input, the model only needs to generate and concatenate values in the "A" and "B" channels; on the other hand, it will be easier for the model to colorize images of varying size, since the rescaled "A" and "B" channels can be concatenated using the original "L" channel with no distortion in pictures.
Project Paper
About
An Image colorization algorithm using PatchGan and Convolution Block Attention Modules (CBAM)
Topics
cbamimage-colorizationpatchgan
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
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
Image-Colorization
Running the Model
  1. Download Places365 to the Dataset directory.
  2. Extract places365_train_standard.txt from places365_train_standard.zip to the Dataset directory.
  3. Start training the model by running all the blocks in UNet_PatchGan_v4.ipnyb.
  4. To evaluate the model with new pictures, modify ckpt_path and img_path in the third and last blocks of Evaluate.ipnyb respectively.
To change the root directory of datasets, modify dataset.py on Line 8 and 34. Be sure to include places365_train_standard.txt in the new directory.
Things to Note
  1. The model is mainly based on PatchGan. A classifier and Convolution Block Attention Module (CBAM) are incorporated to improve the model's performance.
  2. For this model, pictures are represented in LAB color space. There are two main reasons why we utilize LAB instead of RGB: Given that the "L" channel can be used as input, the model only needs to generate and concatenate values in the "A" and "B" channels; on the other hand, it will be easier for the model to colorize images of varying size, since the rescaled "A" and "B" channels can be concatenated using the original "L" channel with no distortion in pictures.
Project Paper
About
An Image colorization algorithm using PatchGan and Convolution Block Attention Modules (CBAM)
Topics
cbamimage-colorizationpatchgan
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
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
Image-Colorization
Running the Model
  1. Download Places365 to the Dataset directory.
  2. Extract places365_train_standard.txt from places365_train_standard.zip to the Dataset directory.
  3. Start training the model by running all the blocks in UNet_PatchGan_v4.ipnyb.
  4. To evaluate the model with new pictures, modify ckpt_path and img_path in the third and last blocks of Evaluate.ipnyb respectively.
To change the root directory of datasets, modify dataset.py on Line 8 and 34. Be sure to include places365_train_standard.txt in the new directory.
Things to Note
  1. The model is mainly based on PatchGan. A classifier and Convolution Block Attention Module (CBAM) are incorporated to improve the model's performance.
  2. For this model, pictures are represented in LAB color space. There are two main reasons why we utilize LAB instead of RGB: Given that the "L" channel can be used as input, the model only needs to generate and concatenate values in the "A" and "B" channels; on the other hand, it will be easier for the model to colorize images of varying size, since the rescaled "A" and "B" channels can be concatenated using the original "L" channel with no distortion in pictures.
Project Paper
About
An Image colorization algorithm using PatchGan and Convolution Block Attention Modules (CBAM)
Topics
cbamimage-colorizationpatchgan
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
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
Image-Colorization
Running the Model
  1. Download Places365 to the Dataset directory.
  2. Extract places365_train_standard.txt from places365_train_standard.zip to the Dataset directory.
  3. Start training the model by running all the blocks in UNet_PatchGan_v4.ipnyb.
  4. To evaluate the model with new pictures, modify ckpt_path and img_path in the third and last blocks of Evaluate.ipnyb respectively.
To change the root directory of datasets, modify dataset.py on Line 8 and 34. Be sure to include places365_train_standard.txt in the new directory.
Things to Note
  1. The model is mainly based on PatchGan. A classifier and Convolution Block Attention Module (CBAM) are incorporated to improve the model's performance.
  2. For this model, pictures are represented in LAB color space. There are two main reasons why we utilize LAB instead of RGB: Given that the "L" channel can be used as input, the model only needs to generate and concatenate values in the "A" and "B" channels; on the other hand, it will be easier for the model to colorize images of varying size, since the rescaled "A" and "B" channels can be concatenated using the original "L" channel with no distortion in pictures.
Project Paper
About
An Image colorization algorithm using PatchGan and Convolution Block Attention Modules (CBAM)
Topics
cbamimage-colorizationpatchgan
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
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
Image-Colorization
Running the Model
  1. Download Places365 to the Dataset directory.
  2. Extract places365_train_standard.txt from places365_train_standard.zip to the Dataset directory.
  3. Start training the model by running all the blocks in UNet_PatchGan_v4.ipnyb.
  4. To evaluate the model with new pictures, modify ckpt_path and img_path in the third and last blocks of Evaluate.ipnyb respectively.
To change the root directory of datasets, modify dataset.py on Line 8 and 34. Be sure to include places365_train_standard.txt in the new directory.
Things to Note
  1. The model is mainly based on PatchGan. A classifier and Convolution Block Attention Module (CBAM) are incorporated to improve the model's performance.
  2. For this model, pictures are represented in LAB color space. There are two main reasons why we utilize LAB instead of RGB: Given that the "L" channel can be used as input, the model only needs to generate and concatenate values in the "A" and "B" channels; on the other hand, it will be easier for the model to colorize images of varying size, since the rescaled "A" and "B" channels can be concatenated using the original "L" channel with no distortion in pictures.
Project Paper
About
An Image colorization algorithm using PatchGan and Convolution Block Attention Modules (CBAM)
Topics
cbamimage-colorizationpatchgan
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Contributors
Languages