https://github.com/codeofrahul/image_captioning_project
This project demonstrates the use of the `blip-image-captioning-base` model, a powerful tool for generating descriptive text captions from images. Built upon the innovative BLIP (Bootstrapping Language-Image Pre-training) architecture, this model excels at understanding and describing visual content.
https://github.com/codeofrahul/image_captioning_project
Last synced: 11 months ago
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This project demonstrates the use of the `blip-image-captioning-base` model, a powerful tool for generating descriptive text captions from images. Built upon the innovative BLIP (Bootstrapping Language-Image Pre-training) architecture, this model excels at understanding and describing visual content.
- Host: GitHub
- URL: https://github.com/codeofrahul/image_captioning_project
- Owner: CodeofRahul
- License: mit
- Created: 2025-02-20T13:14:57.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-02-20T13:23:20.000Z (11 months ago)
- Last Synced: 2025-02-20T14:27:06.915Z (11 months ago)
- Language: Jupyter Notebook
- Size: 7.72 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Image to Text Generation using `blip-image-captioning-base`
## Overview
This project demonstrates the use of the `blip-image-captioning-base` model, a powerful tool for generating descriptive text captions from images. Built upon the innovative BLIP (Bootstrapping Language-Image Pre-training) architecture, this model excels at understanding and describing visual content.
## Key Features
- **Image Captioning:** Generates accurate and context-aware captions for images.
- **Multi-modal Learning:** Leverages both vision and language models for comprehensive understanding.
- **Practical Applications:** Applicable to alt text generation, content categorization, and image search.
## Model Workflow
1. **Vision Encoding:** The image is processed using a Vision Transformer (ViT).
2. **Language Decoding:** A transformer-based language model generates the caption.
3. **End-to-End Process:** The model seamlessly combines visual and language understanding.
## Practical Use Cases
- **Accessibility:** Automates the generation of alt text, enhancing accessibility for visually impaired users.
- **Search Engines:** Improves image indexing and search capabilities by providing relevant descriptions.
- **Content Moderation:** Aids in filtering and categorizing images based on their content.
## Getting Started
1. Install the necessary libraries
2. Import the model and tokenizer
3. Load and preprocess an image
4. Generate the caption
## Example
**Input:** Image of a dog playing with a ball.
**Output:** "A dog playing with a ball on the grass."
## Contributing
Contributions to this project are welcome! Please feel free to open issues or submit pull requests.