From Pixels to Words: Exploring Multi-Modal AI with Python
summary
Live demo shows a Colab Python workflow that loads CLIP (ViT‑B/32) via transformers and torch, preprocesses an online image with PIL/BytesIO, and scores three candidate captions using softmax. Model is pretrained on 400 million image‑text pairs and yields confident similarity probabilities, demonstrating practical zero‑shot performance without fine‑tuning. The notebook installs torch/torchvision/transformers, uses clip-processor to produce PyTorch tensors, and returns logits_per_image that map directly to caption probabilities.
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chapters (14)
- 0:00Introduction
- 1:25What is multimodal AI
- 10:39Multimodal AI challenges
- 14:09CLIP model overview
- 29:19Colab setup
- 31:48Install libraries and import model
- 41:10Load and process image
- 45:27Prepare text prompts
- 48:56CLIP data preprocessing
- 51:20Analyze model results
- 56:16Zero-shot learning demo
- 59:01Test model on ambiguous data
- 1:02:06CLIP limitations and fine-tuning
- 1:03:26Q&A
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