BranchesTags

Latest commit

History

6 Commits

Folders and files

NameName
Last commit message
Last commit date
Image_colourizer.py
Image_colourizer.py
LICENSE
LICENSE
README.md
README.md

Repository files navigation

🎨 Neural Colorizer: U-Net Image Restoration

A minimalist PyTorch implementation of a deep learning model designed to restore color to grayscale images. By utilizing a U-Net style encoder-decoder architecture, this project learns to map single-channel intensity values back to the full RGB spectrum using the CIFAR-10 dataset.

🔬 Architecture & Logic

The model leverages a deep convolutional structure to understand both low-level textures and high-level semantics:

  • The Encoder: A series of Conv2d layers with BatchNorm2d and ReLU activations that compress the input, doubling feature depth at each stage (64 → 128 → 256 → 512).

  • The Decoder: Uses Upsample (bilinear mode) and convolutional layers to reconstruct the spatial resolution while reducing feature depth back to the 3-channel RGB output.

  • Final Layer: Employs a Sigmoid activation function to ensure all output pixel values are normalized between 0 and 1.

🛠️ Technical Specifications

Framework: PyTorch

  • Dataset: CIFAR-10 (sampled to 10,000 images for efficient training)

  • Preprocessing: Images are resized to 32x32 and converted into grayscale/color pairs.

  • Loss Function: L1Loss (Mean Absolute Error) to promote sharper reconstructions.

  • Optimizer: Adam with a learning rate of 0.0005.

  • Hardware: Optimized for CUDA execution.

🚀 Usage

  1. Training The training loop runs for 100 epochs, optimizing the model to minimize the difference between the predicted colorization and the ground truth.
    The model is automatically saved after training torch.save(model.state_dict(), "unet_colorization.pth")

  2. Inference & Visualization The script includes a testing suite that utilizes matplotlib to display a side-by-side comparison of:

  • Grayscale Input: The single-channel source image.

  • Predicted Colorization: The model's interpretation of the scene.

  • Ground Truth: The original color image for accuracy reference.

📊 Performance Visuals

The model outputs a grid of 8 sample images after training to demonstrate its ability to generalize colors across various CIFAR-10 categories like automobiles, animals, and aircraft.

About

An ML model trying its best to make sense of the colourful world

Resources

Readme
GPL-3.0 license
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

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
Latest commit
History
6 Commits
Folders and files
NameName
Last commit message
Last commit date
Image_colourizer.py
Image_colourizer.py
LICENSE
LICENSE
README.md
README.md
Repository files navigation
🎨 Neural Colorizer: U-Net Image Restoration
A minimalist PyTorch implementation of a deep learning model designed to restore color to grayscale images. By utilizing a U-Net style encoder-decoder architecture, this project learns to map single-channel intensity values back to the full RGB spectrum using the CIFAR-10 dataset.
🔬 Architecture & Logic
The model leverages a deep convolutional structure to understand both low-level textures and high-level semantics:
  • The Encoder: A series of Conv2d layers with BatchNorm2d and ReLU activations that compress the input, doubling feature depth at each stage (64 → 128 → 256 → 512).
  • The Decoder: Uses Upsample (bilinear mode) and convolutional layers to reconstruct the spatial resolution while reducing feature depth back to the 3-channel RGB output.
  • Final Layer: Employs a Sigmoid activation function to ensure all output pixel values are normalized between 0 and 1.
🛠️ Technical Specifications
Framework: PyTorch
  • Dataset: CIFAR-10 (sampled to 10,000 images for efficient training)
  • Preprocessing: Images are resized to 32x32 and converted into grayscale/color pairs.
  • Loss Function: L1Loss (Mean Absolute Error) to promote sharper reconstructions.
  • Optimizer: Adam with a learning rate of 0.0005.
  • Hardware: Optimized for CUDA execution.
🚀 Usage
  1. Training
The training loop runs for 100 epochs, optimizing the model to minimize the difference between the predicted colorization and the ground truth.
    
The model is automatically saved after training
torch.save(model.state_dict(), "unet_colorization.pth")
  2. Inference & Visualization
The script includes a testing suite that utilizes matplotlib to display a side-by-side comparison of:
  • Grayscale Input: The single-channel source image.
  • Predicted Colorization: The model's interpretation of the scene.
  • Ground Truth: The original color image for accuracy reference.
📊 Performance Visuals
The model outputs a grid of 8 sample images after training to demonstrate its ability to generalize colors across various CIFAR-10 categories like automobiles, animals, and aircraft.
About
An ML model trying its best to make sense of the colourful world
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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
Latest commit
History
6 Commits
Folders and files
NameName
Last commit message
Last commit date
Image_colourizer.py
Image_colourizer.py
LICENSE
LICENSE
README.md
README.md
Repository files navigation
🎨 Neural Colorizer: U-Net Image Restoration
A minimalist PyTorch implementation of a deep learning model designed to restore color to grayscale images. By utilizing a U-Net style encoder-decoder architecture, this project learns to map single-channel intensity values back to the full RGB spectrum using the CIFAR-10 dataset.
🔬 Architecture & Logic
The model leverages a deep convolutional structure to understand both low-level textures and high-level semantics:
  • The Encoder: A series of Conv2d layers with BatchNorm2d and ReLU activations that compress the input, doubling feature depth at each stage (64 → 128 → 256 → 512).
  • The Decoder: Uses Upsample (bilinear mode) and convolutional layers to reconstruct the spatial resolution while reducing feature depth back to the 3-channel RGB output.
  • Final Layer: Employs a Sigmoid activation function to ensure all output pixel values are normalized between 0 and 1.
🛠️ Technical Specifications
Framework: PyTorch
  • Dataset: CIFAR-10 (sampled to 10,000 images for efficient training)
  • Preprocessing: Images are resized to 32x32 and converted into grayscale/color pairs.
  • Loss Function: L1Loss (Mean Absolute Error) to promote sharper reconstructions.
  • Optimizer: Adam with a learning rate of 0.0005.
  • Hardware: Optimized for CUDA execution.
🚀 Usage
  1. Training
The training loop runs for 100 epochs, optimizing the model to minimize the difference between the predicted colorization and the ground truth.
    
The model is automatically saved after training
torch.save(model.state_dict(), "unet_colorization.pth")
  2. Inference & Visualization
The script includes a testing suite that utilizes matplotlib to display a side-by-side comparison of:
  • Grayscale Input: The single-channel source image.
  • Predicted Colorization: The model's interpretation of the scene.
  • Ground Truth: The original color image for accuracy reference.
📊 Performance Visuals
The model outputs a grid of 8 sample images after training to demonstrate its ability to generalize colors across various CIFAR-10 categories like automobiles, animals, and aircraft.
About
An ML model trying its best to make sense of the colourful world
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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
Latest commit
History
6 Commits
Folders and files
NameName
Last commit message
Last commit date
Image_colourizer.py
Image_colourizer.py
LICENSE
LICENSE
README.md
README.md
Repository files navigation
🎨 Neural Colorizer: U-Net Image Restoration
A minimalist PyTorch implementation of a deep learning model designed to restore color to grayscale images. By utilizing a U-Net style encoder-decoder architecture, this project learns to map single-channel intensity values back to the full RGB spectrum using the CIFAR-10 dataset.
🔬 Architecture & Logic
The model leverages a deep convolutional structure to understand both low-level textures and high-level semantics:
  • The Encoder: A series of Conv2d layers with BatchNorm2d and ReLU activations that compress the input, doubling feature depth at each stage (64 → 128 → 256 → 512).
  • The Decoder: Uses Upsample (bilinear mode) and convolutional layers to reconstruct the spatial resolution while reducing feature depth back to the 3-channel RGB output.
  • Final Layer: Employs a Sigmoid activation function to ensure all output pixel values are normalized between 0 and 1.
🛠️ Technical Specifications
Framework: PyTorch
  • Dataset: CIFAR-10 (sampled to 10,000 images for efficient training)
  • Preprocessing: Images are resized to 32x32 and converted into grayscale/color pairs.
  • Loss Function: L1Loss (Mean Absolute Error) to promote sharper reconstructions.
  • Optimizer: Adam with a learning rate of 0.0005.
  • Hardware: Optimized for CUDA execution.
🚀 Usage
  1. Training
The training loop runs for 100 epochs, optimizing the model to minimize the difference between the predicted colorization and the ground truth.
    
The model is automatically saved after training
torch.save(model.state_dict(), "unet_colorization.pth")
  2. Inference & Visualization
The script includes a testing suite that utilizes matplotlib to display a side-by-side comparison of:
  • Grayscale Input: The single-channel source image.
  • Predicted Colorization: The model's interpretation of the scene.
  • Ground Truth: The original color image for accuracy reference.
📊 Performance Visuals
The model outputs a grid of 8 sample images after training to demonstrate its ability to generalize colors across various CIFAR-10 categories like automobiles, animals, and aircraft.
About
An ML model trying its best to make sense of the colourful world
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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
Latest commit
History
6 Commits
Folders and files
NameName
Last commit message
Last commit date
Image_colourizer.py
Image_colourizer.py
LICENSE
LICENSE
README.md
README.md
Repository files navigation
🎨 Neural Colorizer: U-Net Image Restoration
A minimalist PyTorch implementation of a deep learning model designed to restore color to grayscale images. By utilizing a U-Net style encoder-decoder architecture, this project learns to map single-channel intensity values back to the full RGB spectrum using the CIFAR-10 dataset.
🔬 Architecture & Logic
The model leverages a deep convolutional structure to understand both low-level textures and high-level semantics:
  • The Encoder: A series of Conv2d layers with BatchNorm2d and ReLU activations that compress the input, doubling feature depth at each stage (64 → 128 → 256 → 512).
  • The Decoder: Uses Upsample (bilinear mode) and convolutional layers to reconstruct the spatial resolution while reducing feature depth back to the 3-channel RGB output.
  • Final Layer: Employs a Sigmoid activation function to ensure all output pixel values are normalized between 0 and 1.
🛠️ Technical Specifications
Framework: PyTorch
  • Dataset: CIFAR-10 (sampled to 10,000 images for efficient training)
  • Preprocessing: Images are resized to 32x32 and converted into grayscale/color pairs.
  • Loss Function: L1Loss (Mean Absolute Error) to promote sharper reconstructions.
  • Optimizer: Adam with a learning rate of 0.0005.
  • Hardware: Optimized for CUDA execution.
🚀 Usage
  1. Training
The training loop runs for 100 epochs, optimizing the model to minimize the difference between the predicted colorization and the ground truth.
    
The model is automatically saved after training
torch.save(model.state_dict(), "unet_colorization.pth")
  2. Inference & Visualization
The script includes a testing suite that utilizes matplotlib to display a side-by-side comparison of:
  • Grayscale Input: The single-channel source image.
  • Predicted Colorization: The model's interpretation of the scene.
  • Ground Truth: The original color image for accuracy reference.
📊 Performance Visuals
The model outputs a grid of 8 sample images after training to demonstrate its ability to generalize colors across various CIFAR-10 categories like automobiles, animals, and aircraft.
About
An ML model trying its best to make sense of the colourful world
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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
Latest commit
History
6 Commits
Folders and files
NameName
Last commit message
Last commit date
Image_colourizer.py
Image_colourizer.py
LICENSE
LICENSE
README.md
README.md
Repository files navigation
🎨 Neural Colorizer: U-Net Image Restoration
A minimalist PyTorch implementation of a deep learning model designed to restore color to grayscale images. By utilizing a U-Net style encoder-decoder architecture, this project learns to map single-channel intensity values back to the full RGB spectrum using the CIFAR-10 dataset.
🔬 Architecture & Logic
The model leverages a deep convolutional structure to understand both low-level textures and high-level semantics:
  • The Encoder: A series of Conv2d layers with BatchNorm2d and ReLU activations that compress the input, doubling feature depth at each stage (64 → 128 → 256 → 512).
  • The Decoder: Uses Upsample (bilinear mode) and convolutional layers to reconstruct the spatial resolution while reducing feature depth back to the 3-channel RGB output.
  • Final Layer: Employs a Sigmoid activation function to ensure all output pixel values are normalized between 0 and 1.
🛠️ Technical Specifications
Framework: PyTorch
  • Dataset: CIFAR-10 (sampled to 10,000 images for efficient training)
  • Preprocessing: Images are resized to 32x32 and converted into grayscale/color pairs.
  • Loss Function: L1Loss (Mean Absolute Error) to promote sharper reconstructions.
  • Optimizer: Adam with a learning rate of 0.0005.
  • Hardware: Optimized for CUDA execution.
🚀 Usage
  1. Training
The training loop runs for 100 epochs, optimizing the model to minimize the difference between the predicted colorization and the ground truth.
    
The model is automatically saved after training
torch.save(model.state_dict(), "unet_colorization.pth")
  2. Inference & Visualization
The script includes a testing suite that utilizes matplotlib to display a side-by-side comparison of:
  • Grayscale Input: The single-channel source image.
  • Predicted Colorization: The model's interpretation of the scene.
  • Ground Truth: The original color image for accuracy reference.
📊 Performance Visuals
The model outputs a grid of 8 sample images after training to demonstrate its ability to generalize colors across various CIFAR-10 categories like automobiles, animals, and aircraft.
About
An ML model trying its best to make sense of the colourful world
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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
Latest commit
History
6 Commits
Folders and files
NameName
Last commit message
Last commit date
Image_colourizer.py
Image_colourizer.py
LICENSE
LICENSE
README.md
README.md
Repository files navigation
🎨 Neural Colorizer: U-Net Image Restoration
A minimalist PyTorch implementation of a deep learning model designed to restore color to grayscale images. By utilizing a U-Net style encoder-decoder architecture, this project learns to map single-channel intensity values back to the full RGB spectrum using the CIFAR-10 dataset.
🔬 Architecture & Logic
The model leverages a deep convolutional structure to understand both low-level textures and high-level semantics:
  • The Encoder: A series of Conv2d layers with BatchNorm2d and ReLU activations that compress the input, doubling feature depth at each stage (64 → 128 → 256 → 512).
  • The Decoder: Uses Upsample (bilinear mode) and convolutional layers to reconstruct the spatial resolution while reducing feature depth back to the 3-channel RGB output.
  • Final Layer: Employs a Sigmoid activation function to ensure all output pixel values are normalized between 0 and 1.
🛠️ Technical Specifications
Framework: PyTorch
  • Dataset: CIFAR-10 (sampled to 10,000 images for efficient training)
  • Preprocessing: Images are resized to 32x32 and converted into grayscale/color pairs.
  • Loss Function: L1Loss (Mean Absolute Error) to promote sharper reconstructions.
  • Optimizer: Adam with a learning rate of 0.0005.
  • Hardware: Optimized for CUDA execution.
🚀 Usage
  1. Training
The training loop runs for 100 epochs, optimizing the model to minimize the difference between the predicted colorization and the ground truth.
    
The model is automatically saved after training
torch.save(model.state_dict(), "unet_colorization.pth")
  2. Inference & Visualization
The script includes a testing suite that utilizes matplotlib to display a side-by-side comparison of:
  • Grayscale Input: The single-channel source image.
  • Predicted Colorization: The model's interpretation of the scene.
  • Ground Truth: The original color image for accuracy reference.
📊 Performance Visuals
The model outputs a grid of 8 sample images after training to demonstrate its ability to generalize colors across various CIFAR-10 categories like automobiles, animals, and aircraft.
About
An ML model trying its best to make sense of the colourful world
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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
Latest commit
History
6 Commits
Folders and files
NameName
Last commit message
Last commit date
Image_colourizer.py
Image_colourizer.py
LICENSE
LICENSE
README.md
README.md
Repository files navigation
🎨 Neural Colorizer: U-Net Image Restoration
A minimalist PyTorch implementation of a deep learning model designed to restore color to grayscale images. By utilizing a U-Net style encoder-decoder architecture, this project learns to map single-channel intensity values back to the full RGB spectrum using the CIFAR-10 dataset.
🔬 Architecture & Logic
The model leverages a deep convolutional structure to understand both low-level textures and high-level semantics:
  • The Encoder: A series of Conv2d layers with BatchNorm2d and ReLU activations that compress the input, doubling feature depth at each stage (64 → 128 → 256 → 512).
  • The Decoder: Uses Upsample (bilinear mode) and convolutional layers to reconstruct the spatial resolution while reducing feature depth back to the 3-channel RGB output.
  • Final Layer: Employs a Sigmoid activation function to ensure all output pixel values are normalized between 0 and 1.
🛠️ Technical Specifications
Framework: PyTorch
  • Dataset: CIFAR-10 (sampled to 10,000 images for efficient training)
  • Preprocessing: Images are resized to 32x32 and converted into grayscale/color pairs.
  • Loss Function: L1Loss (Mean Absolute Error) to promote sharper reconstructions.
  • Optimizer: Adam with a learning rate of 0.0005.
  • Hardware: Optimized for CUDA execution.
🚀 Usage
  1. Training
The training loop runs for 100 epochs, optimizing the model to minimize the difference between the predicted colorization and the ground truth.
    
The model is automatically saved after training
torch.save(model.state_dict(), "unet_colorization.pth")
  2. Inference & Visualization
The script includes a testing suite that utilizes matplotlib to display a side-by-side comparison of:
  • Grayscale Input: The single-channel source image.
  • Predicted Colorization: The model's interpretation of the scene.
  • Ground Truth: The original color image for accuracy reference.
📊 Performance Visuals
The model outputs a grid of 8 sample images after training to demonstrate its ability to generalize colors across various CIFAR-10 categories like automobiles, animals, and aircraft.
About
An ML model trying its best to make sense of the colourful world
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages