THE FUTURE IS HERE

CLIP, T-SNE, and UMAP – Master Image Embeddings & Vector Analysis

Description:

Start your Data Science and Computer Vision adventure with this comprehensive Image Embedding and Vector Analysis guide. Explore OpenAI CLIP embeddings for image clustering and duplicate detection, and learn essential concepts like T-SNE, UMAP, and MNIST. Follow our beginner-friendly Google Colab notebook to master the basics and kickstart your journey in Computer Vision!

Chapters:

00:00 Introduction
01:23 Python Environment Setup
01:58 Clustering MNIST images using pixel brightness
09:00 T-SNE vs. UMAP
10:40 Clustering images using OpenAI CLIP embeddings
17:22: Using OpenAI CLIP embeddings to detect duplicates or close duplicates
20:05 Conclusions

Resources:

🌏 Roboflow: https://roboflow.com
🌌 Roboflow Universe: https://universe.roboflow.com

📓 “Image Embeddings Analysis notebook”: https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/image_embeddings_analysis_part_1.ipynb
📚 “What is OpenAI’s CLIP and how to use it?” blog post: https://blog.roboflow.com/openai-clip
🎬 “CLIP: OpenAI’s amazing new zero-shot image classifier” video: https://youtu.be/8o701AEoZ8I

Stay updated with the projects I’m working on at https://github.com/roboflow and https://github.com/SkalskiP! ⭐

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ComputerVision #DataScience #ImageEmbeddings #OpenAICLIP #TSNE #UMAP #MNIST