Wildlife BLIP-2 Fine-Tuning Project

This project demonstrates how to fine-tune the BLIP-2 model for wildlife image captioning and visual question answering (VQA) using your own dataset, and provides a user-friendly web interface for testing your model.

Folder Structure

project/
│
├── images/             # Place your images here
│   ├── elephant.jpg
│   ├── deer.jpg
│   └── ...
│
├── data.json           # Annotations file (see format below)
├── finetune_blip2.py   # Fine-tuning script
├── app.py              # Streamlit web app for inference
├── requirements.txt    # Python dependencies
└── README.md           # This file

Dataset Format

Example data.json:

[
  {
    "image": "images/elephant.jpg",
    "caption": "A herd of elephants crossing the river."
  },
  {
    "image": "images/deer.jpg",
    "question": "Is there a deer in the image?",
    "answer": "Yes"
  }
]

• For image captioning, use the caption field.
• For VQA, use question and answer fields.

Setup Environment

Install the required packages:

pip install -r requirements.txt

Fine-Tuning

Run the fine-tuning script:

python finetune_blip2.py

Web Frontend (Streamlit)

Run the Streamlit app for batch image upload, gallery, and history features:

streamlit run app.py

• Upload one or more images.
• Select Caption or VQA mode.
• For VQA, enter your question.
• View results, gallery, and history in the web UI.

Inference Example (Python)

After training, you can generate captions or answers using the model in Python. See finetune_blip2.py for details.

Tips

• Add more images and annotations to improve performance.
• You can use the same script for both captioning and VQA tasks.
• For deployment, consider converting the model to TorchScript or ONNX for edge devices.

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