OpenAI CLIP Model Explained: Architecture and Python Implementation

In this video, we break down how CLIP (Contrastive Language–Image Pretraining) works — and then build a simplified prototype to help you deeply understand the core training logic.

🚀 What you’ll learn:
How CLIP uses contrastive learning to align images and text in a shared embedding space
How the architecture works: dual encoders, projection layers, and a similarity matrix
How temperature scaling shapes softmax predictions
How to compute cross-entropy loss from both image→text and text→image directions
What gets updated during backpropagation (yes, even the temperature!)
How to implement the core training loop with dummy encoders and a toy dataset

Links:
1. Colab Notebook: https://colab.research.google.com/dri...
2. Open AI CLIP: https://openai.com/index/clip/

Chapters

00:00 Intro
00:27 Contrastive Learning
01:06 Dataset Collection
01:34 Architecture
02:40 Training Loop Explained
03:29 Temperature Parameter
04:03 CLIP in Python and Torch Overview
05:14 Training Loop in Python
07:23 Implement L2, Softmax, and Cross Entropy
11:07 Numerically Stable Softmax and Cross Entropy
13:03 CLIP Module: _init_ and forward

🧠 Key Concepts Covered:
Contrastive loss
Scaled cosine similarity
Shared embedding space
Learnable temperature parameter

🔧 Hands-on Section:We’ll code the training loop step-by-step using Python, PyTorch, Jupyter Notebook, and a toy dataset — so you can build intuition and gain a practical understanding of how CLIP learns from scratch.

🔜 Coming next:We’ll plug in lightweight pretrained encoders to upgrade this prototype.


—
📚 Perfect if you want to understand CLIP at its core and build a working foundation for multimodal learning.
👍 Like, comment, and subscribe for more deep learning breakdowns and code-first explorations!
#CLIP #ContrastiveLearning #MultimodalAI #DeepLearning #MachineLearning #MLTutorial #PyTorch #Python #JupyterNotebook #AI #ml #gpt