OpenAI CLIP model explained | Contrastive Learning | Architecture
Understanding 𝐂𝐋𝐈𝐏 & 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐢𝐭 𝐟𝐫𝐨𝐦 𝐒𝐜𝐫𝐚𝐭𝐜𝐡
Computer vision has evolved from recognizing fixed categories to understanding the relationship between images and text.
I have implemented CLIP model from scratch, you can fine the code on my
𝐆𝐢𝐭𝐡𝐮𝐛
https://github.com/Mayankpratapsingh022/Deep-Learning-from-Scratch
CLIP (𝘊𝘰𝘯𝘵𝘳𝘢𝘴𝘵𝘪𝘷𝘦 𝘓𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘐𝘮𝘢𝘨𝘦 𝘗𝘳𝘦𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨) represents this revolutionary shift.
𝐖𝐡𝐚𝐭 𝐢𝐬 𝐂𝐋𝐈𝐏?
CLIP is an AI model that learns to connect images with their natural language descriptions.
Unlike traditional models limited to predefined categories, CLIP understands images through text, enabling open world recognition without retraining.
𝐇𝐨𝐰 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬
The architecture consists of two key components:
𝐈𝐦𝐚𝐠𝐞 𝐄𝐧𝐜𝐨𝐝𝐞𝐫:
Processes visual information using Vision Transformers, converting images into mathematical representations
𝐓𝐞𝐱𝐭 𝐄𝐧𝐜𝐨𝐝𝐞𝐫:
Transforms text descriptions into comparable mathematical formats using Transformer architecture
CLIP learns by looking at millions of image text pairs, figuring out which words belong with which pictures.
- It creates a shared understanding space where:
- Images get converted to numbers
- Text gets converted to the same type of numbers
- Matching pairs are pulled closer together
- Non matching pairs are pushed apart
𝐑𝐞𝐚𝐥 𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬
Zero Shot Classification:
Classify images into categories never seen during training simply by providing text descriptions
Content Moderation:
Automatically identify and filter inappropriate content across platforms
Visual Search:
Find images using natural language queries instead of keywords
Accessibility Tools:
Generate descriptions for visually impaired users
Creative Applications:
Power text to image generation systems and visual content creation
Subscribe to my channel for more such in-depth explainer videos on AI and CS
Understanding 𝐂𝐋𝐈𝐏 & 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐢𝐭 𝐟𝐫𝐨𝐦 𝐒𝐜𝐫𝐚𝐭𝐜𝐡
Computer vision has evolved from recognizing fixed categories to understanding the relationship between images and text.
I have implemented CLIP model from scratch, you can fine the code on my
𝐆𝐢𝐭𝐡𝐮𝐛
https://github.com/Mayankpratapsingh022/Deep-Learning-from-Scratch
CLIP (𝘊𝘰𝘯𝘵𝘳𝘢𝘴𝘵𝘪𝘷𝘦 𝘓𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘐𝘮𝘢𝘨𝘦 𝘗𝘳𝘦𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨) represents this revolutionary shift.
𝐖𝐡𝐚𝐭 𝐢𝐬 𝐂𝐋𝐈𝐏?
CLIP is an AI model that learns to connect images with their natural language descriptions.
Unlike traditional models limited to predefined categories, CLIP understands images through text, enabling open world recognition without retraining.
𝐇𝐨𝐰 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬
The architecture consists of two key components:
𝐈𝐦𝐚𝐠𝐞 𝐄𝐧𝐜𝐨𝐝𝐞𝐫:
Processes visual information using Vision Transformers, converting images into mathematical representations
𝐓𝐞𝐱𝐭 𝐄𝐧𝐜𝐨𝐝𝐞𝐫:
Transforms text descriptions into comparable mathematical formats using Transformer architecture
CLIP learns by looking at millions of image text pairs, figuring out which words belong with which pictures.
– It creates a shared understanding space where:
– Images get converted to numbers
– Text gets converted to the same type of numbers
– Matching pairs are pulled closer together
– Non matching pairs are pushed apart
𝐑𝐞𝐚𝐥 𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬
Zero Shot Classification:
Classify images into categories never seen during training simply by providing text descriptions
Content Moderation:
Automatically identify and filter inappropriate content across platforms
Visual Search:
Find images using natural language queries instead of keywords
Accessibility Tools:
Generate descriptions for visually impaired users
Creative Applications:
Power text to image generation systems and visual content creation
Subscribe to my channel for more such in-depth explainer videos on AI and CS
