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CLIP From Scratch

This is my implementation of the CLIP (Contrastive Language-Image Pretraining) model.

Project Structure

  • clip_deployment/
    Contains a Gradio app that lets you upload an image and returns the top 5 matching captions from a pre-stored captions list.

  • CLIP_training.ipynb
    A Jupyter notebook where the entire training process of the CLIP model is implemented.

How to Use

  1. Training
    Open and run the CLIP_training.ipynb notebook to train the CLIP model on your dataset.

  2. Deployment
    Inside the clip_deployment folder, launch the Gradio app to interact with the trained model by uploading images and getting the most relevant captions.

Feel free to explore, experiment on this project!

About

No description, website, or topics provided.

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
GitHub - prashere/CLIP · GitHub
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README.md
README.md
Repository files navigation
CLIP From Scratch
This is my implementation of the CLIP (Contrastive Language-Image Pretraining) model.
Project Structure
  • clip_deployment/
    
Contains a Gradio app that lets you upload an image and returns the top 5 matching captions from a pre-stored captions list.
  • CLIP_training.ipynb
    
A Jupyter notebook where the entire training process of the CLIP model is implemented.
How to Use
  1. Training
    
Open and run the CLIP_training.ipynb notebook to train the CLIP model on your dataset.
  2. Deployment
    
Inside the clip_deployment folder, launch the Gradio app to interact with the trained model by uploading images and getting the most relevant captions.
Feel free to explore, experiment on this project!
About
No description, website, or topics provided.
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Readme
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1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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var style = document.createElement('style');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - prashere/CLIP · GitHub
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CLIP_training.ipynb
README.md
README.md
Repository files navigation
CLIP From Scratch
This is my implementation of the CLIP (Contrastive Language-Image Pretraining) model.
Project Structure
  • clip_deployment/
    
Contains a Gradio app that lets you upload an image and returns the top 5 matching captions from a pre-stored captions list.
  • CLIP_training.ipynb
    
A Jupyter notebook where the entire training process of the CLIP model is implemented.
How to Use
  1. Training
    
Open and run the CLIP_training.ipynb notebook to train the CLIP model on your dataset.
  2. Deployment
    
Inside the clip_deployment folder, launch the Gradio app to interact with the trained model by uploading images and getting the most relevant captions.
Feel free to explore, experiment on this project!
About
No description, website, or topics provided.
Resources
Readme
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Watchers
1 watching
Forks
0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - prashere/CLIP · GitHub
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CLIP_training.ipynb
README.md
README.md
Repository files navigation
CLIP From Scratch
This is my implementation of the CLIP (Contrastive Language-Image Pretraining) model.
Project Structure
  • clip_deployment/
    
Contains a Gradio app that lets you upload an image and returns the top 5 matching captions from a pre-stored captions list.
  • CLIP_training.ipynb
    
A Jupyter notebook where the entire training process of the CLIP model is implemented.
How to Use
  1. Training
    
Open and run the CLIP_training.ipynb notebook to train the CLIP model on your dataset.
  2. Deployment
    
Inside the clip_deployment folder, launch the Gradio app to interact with the trained model by uploading images and getting the most relevant captions.
Feel free to explore, experiment on this project!
About
No description, website, or topics provided.
Resources
Readme
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Stars
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Watchers
1 watching
Forks
0 forks
Report repository
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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',
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - prashere/CLIP · GitHub
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CLIP_training.ipynb
CLIP_training.ipynb
README.md
README.md
Repository files navigation
CLIP From Scratch
This is my implementation of the CLIP (Contrastive Language-Image Pretraining) model.
Project Structure
  • clip_deployment/
    
Contains a Gradio app that lets you upload an image and returns the top 5 matching captions from a pre-stored captions list.
  • CLIP_training.ipynb
    
A Jupyter notebook where the entire training process of the CLIP model is implemented.
How to Use
  1. Training
    
Open and run the CLIP_training.ipynb notebook to train the CLIP model on your dataset.
  2. Deployment
    
Inside the clip_deployment folder, launch the Gradio app to interact with the trained model by uploading images and getting the most relevant captions.
Feel free to explore, experiment on this project!
About
No description, website, or topics provided.
Resources
Readme
Activity
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Watchers
1 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
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function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - prashere/CLIP · GitHub
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CLIP_training.ipynb
CLIP_training.ipynb
README.md
README.md
Repository files navigation
CLIP From Scratch
This is my implementation of the CLIP (Contrastive Language-Image Pretraining) model.
Project Structure
  • clip_deployment/
    
Contains a Gradio app that lets you upload an image and returns the top 5 matching captions from a pre-stored captions list.
  • CLIP_training.ipynb
    
A Jupyter notebook where the entire training process of the CLIP model is implemented.
How to Use
  1. Training
    
Open and run the CLIP_training.ipynb notebook to train the CLIP model on your dataset.
  2. Deployment
    
Inside the clip_deployment folder, launch the Gradio app to interact with the trained model by uploading images and getting the most relevant captions.
Feel free to explore, experiment on this project!
About
No description, website, or topics provided.
Resources
Readme
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1 watching
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0 forks
Report repository
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, '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';
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var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - prashere/CLIP · GitHub
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README.md
Repository files navigation
CLIP From Scratch
This is my implementation of the CLIP (Contrastive Language-Image Pretraining) model.
Project Structure
  • clip_deployment/
    
Contains a Gradio app that lets you upload an image and returns the top 5 matching captions from a pre-stored captions list.
  • CLIP_training.ipynb
    
A Jupyter notebook where the entire training process of the CLIP model is implemented.
How to Use
  1. Training
    
Open and run the CLIP_training.ipynb notebook to train the CLIP model on your dataset.
  2. Deployment
    
Inside the clip_deployment folder, launch the Gradio app to interact with the trained model by uploading images and getting the most relevant captions.
Feel free to explore, experiment on this project!
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
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Watchers
1 watching
Forks
0 forks
Report repository
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, '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 - prashere/CLIP · GitHub
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NameName
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clip_deployment
CLIP_training.ipynb
CLIP_training.ipynb
README.md
README.md
Repository files navigation
CLIP From Scratch
This is my implementation of the CLIP (Contrastive Language-Image Pretraining) model.
Project Structure
  • clip_deployment/
    
Contains a Gradio app that lets you upload an image and returns the top 5 matching captions from a pre-stored captions list.
  • CLIP_training.ipynb
    
A Jupyter notebook where the entire training process of the CLIP model is implemented.
How to Use
  1. Training
    
Open and run the CLIP_training.ipynb notebook to train the CLIP model on your dataset.
  2. Deployment
    
Inside the clip_deployment folder, launch the Gradio app to interact with the trained model by uploading images and getting the most relevant captions.
Feel free to explore, experiment on this project!
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
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Watchers
1 watching
Forks
0 forks
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