🔍 VLM Fine-Tuning Using BLIP

Welcome to this project showcasing Vision-Language Model (VLM) fine-tuning using BLIP (Bootstrapped Language-Image Pretraining). This notebook offers a step-by-step tutorial on how to fine-tune BLIP models for vision-language tasks, empowering you to build intelligent, multimodal systems that understand both text and images.

---

📌 Highlights

• ✅ Hands-on fine-tuning of BLIP models on custom data
• 📷 Support for multimodal image-text datasets
• 🧠 Built for experimentation with vision-language tasks (captioning, retrieval, VQA)
• 🔄 Easily extendable for other VLM models or datasets

---

📂 Project Structure

VLM_Finetuning_Using_BLIP.ipynb    # Jupyter notebook with full fine-tuning pipeline
requirements.txt                   # Required Python libraries (create if missing)
data/                              # Folder for images and captions

---

🧪 What You’ll Learn

• How to set up and use BLIP for fine-tuning
• Data preparation techniques for multimodal learning
• Training loop and optimization details
• Evaluation strategies for vision-language tasks
• How to modify and extend the model for your own needs

---

⚙️ Getting Started

1. Clone the Repository

git clone https://github.com/your-username/vlm-finetuning-blip.git
cd vlm-finetuning-blip

2. Set Up the Environment

pip install -r requirements.txt

3. Launch the Notebook

jupyter notebook VLM_Finetuning_Using_BLIP.ipynb

---

🧰 Dependencies

• Python >= 3.8
• PyTorch
• Transformers
• torchvision
• Jupyter

(Adjust based on the actual content of your notebook)

---

📊 Use Cases

• 📸 Image Captioning
• 🔍 Image-Text Retrieval
• ❓ Visual Question Answering (VQA)
• 🧩 Multimodal Representation Learning

---

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

---

🙌 Acknowledgements

• BLIP
• Hugging Face Transformers
• All contributors and open-source heroes

---

✨ Feel free to ⭐️ this repository if you find it helpful or inspiring!