🖼️ AutoImageCaptioning
AutoImageCaptioning is a simple yet powerful tool that automatically generates captions for images using the BLIP (Bootstrapping Language-Image Pretraining) model. Lightweight and efficient, it’s perfect for beginners and small-scale projects, requiring minimal storage and computation.
🔗 Live Demo: Check it out here: https://huggingface.co/spaces/alpha203/AutoImageCaptioning
🚀 Features ✅ Automatic Caption Generation – Generate meaningful captions for images.
✅ BLIP Model – Uses the Salesforce/blip-image-captioning-base model for high-quality captions.
✅ Optimized for Efficiency – Runs smoothly on CPU/GPU with minimal computational overhead.
✅ Minimal Dependencies – Requires only torch, transformers, and PIL for execution.
✅ Beginner-Friendly – Simple and easy to integrate into any project.
🛠️ Installation
To use this model, install the required dependencies using: BASH
pip install torch torchvision transformers pillow
📸 Usage
1️⃣ Import & Load the Model: python
from caption_generator import generate_caption
2️⃣ Generate a Caption for an Image: python
caption = generate_caption("your_image.jpg") / In Updated Version # A New feature to uplaod using
print("Generated Caption:", caption)
SAMPLE OUTPUTS:
**📂 Project Structure: **
AutoImageCaptioning/
│── caption_generator.py /.ipynb(UPDATED VERSION) # Core Python script for caption generation
│── test.jpg # Sample image (replace with your own)
│── README.md # Project documentation
🎯 How It Works
1️⃣ The script loads an image and preprocesses it.
2️⃣ The BLIP model generates a textual description of the image.
3️⃣ The output caption is printed or stored for further use.
🏗️ Future Improvements
🔹 Optimize the model for lower latency and memory usage.
🔹 Add support for batch processing of multiple images.
🔹 Implement a simple web interface for easy image uploads.
🔹 Explore fine-tuning the model for domain-specific captioning.
🤝 Contributing
Contributions are welcome! If you happen to have any improvements, feel free to submit a pull request.