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Artificial Intelligence · Mini Project
Image Caption Recommender
Suggests the best-fitting social media caption for a photo using CLIP
1-2 weeks build
Overview
Rather than generating text, this kit retrieves it. A bank of a few thousand captions is embedded once with OpenAI's open-source CLIP model, and each uploaded photo is embedded into the same space and matched by cosine similarity. Users can filter by mood such as funny, travel or motivational and get the five closest captions.
What makes it stand up
- 01Retrieval-based design with no text generation needed
- 02CLIP image and text embeddings in one shared space
- 03FAISS index for instant nearest-neighbour lookup
- 04Mood and hashtag filters on the caption bank
- 05Top-five suggestions with similarity scores
Module breakdown
- 01
Caption bank
Curated captions tagged by mood and theme.
- 02
Embedding
CLIP encoders for images and captions.
- 03
Index
FAISS inner-product index over normalised vectors.
- 04
Ranking
Cosine similarity with mood filters applied.
- 05
App
Streamlit upload with copy-to-clipboard suggestions.
After this, you will be able to
- Explain contrastive learning and why CLIP aligns images with text
- Compare retrieval against generation for this use case
- Demo caption suggestions for a photo from your phone
Technology stack
- Python
- PyTorch
- CLIP
- FAISS
- Streamlit
- Level
- Mini Project
- Domain
- Artificial Intelligence
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support