BLIP VQA Fine-tuned Model - Draft 1

This model generates structured question-answer pairs from input images, fine-tuned for visual question answering tasks with structured output format.

Model Details

Model Description

This is a fine-tuned BLIP model specifically designed for Visual Question Answering (VQA) tasks. The model takes images as input and generates structured question-answer pairs in XML-like format with <question> and <answer> tags.

Developed by: eagle0504
Model type: Vision-Language Model (BLIP-based VQA)
Language(s): English
License: Apache 2.0
Finetuned from model: BLIP (Bootstrapping Language-Image Pre-training)

Model Sources

Repository: https://huggingface.co/eagle0504/blip-vqa-finetuned-draft-1
Base Model: BLIP (Salesforce)

Uses

Direct Use

The model can be used for:

Automated question-answer generation from images
Visual content analysis and understanding
Educational content creation from visual materials
Image captioning with structured Q&A format

Usage Example

from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import torch

# Load model and processor
model = AutoModelForVision2Seq.from_pretrained("eagle0504/blip-vqa-finetuned-draft-1")
processor = AutoProcessor.from_pretrained("eagle0504/blip-vqa-finetuned-draft-1")

# Load and process image
image = Image.open("path/to/your/image.jpg")
inputs = processor(images=image, return_tensors="pt")

# Generate question-answer pair
with torch.no_grad():
    generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)
    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
    
print(generated_text)
# Output: <question>what is there in the figure?</question><answer>the entire thickness of the epithelium</answer>

Out-of-Scope Use

The model is not designed for real-time applications requiring immediate responses
Not suitable for generating factual information that requires external knowledge beyond visual content
May not perform well on images significantly different from the training distribution
Should not be used for critical decision-making without human oversight

Training Details

Training Data

The model was fine-tuned on a dataset containing:

Dataset size: 19,654 image-question-answer triplets
Features: Images paired with corresponding questions and answers
Format: Structured data with XML-like tags for questions and answers

Training Procedure

Training Hyperparameters

Training regime: Mixed precision training (fp16/bf16)
Max sequence length: 50 tokens
Batch size: [Your batch size]
Learning rate: [Your learning rate]
Epochs: [Number of epochs]

Preprocessing

Images are processed using the model's associated processor, which handles:

Image resizing and normalization
Tensor conversion for model input
Text tokenization for question-answer pairs

Performance

The model generates structured question-answer pairs with the following format:

<question>[Generated question about the image]</question><answer>[Corresponding answer]</answer>

Evaluation Metrics

Text generation quality
Semantic relevance of questions to image content
Accuracy of answers relative to visual content
Format consistency (proper XML tag structure)

Bias, Risks, and Limitations

Known Limitations

Performance may vary based on image quality and complexity
Generated questions and answers reflect patterns in training data
May produce repetitive or generic questions for certain image types
Limited to the vocabulary and concepts present in training data

Recommendations

Validate outputs for critical applications
Consider domain-specific fine-tuning for specialized use cases
Review generated content for appropriateness in your specific context

Technical Specifications

Model Architecture

Base architecture: BLIP (Bootstrapping Language-Image Pre-training)
Vision Encoder: ViT (Vision Transformer)
Text Decoder: BERT-based decoder
Input: RGB images (224x224 default resolution)
Output: Text sequences with structured Q&A format (<question> and <answer> tags)
Fine-tuned for Visual Question Answering with structured output

Compute Infrastructure

Hardware Requirements

GPU memory: Minimum 4GB for inference
CPU: Compatible with modern processors
RAM: 8GB+ recommended for optimal performance

Software Dependencies

transformers>=4.35.0
torch>=2.0.0
Pillow>=8.0.0

Citation

If you use this model in your research, please cite:

@misc{eagle0504-blip-vqa-2025,
  title={BLIP VQA Fine-tuned Model - Draft 1},
  author={eagle0504},
  year={2025},
  url={https://huggingface.co/eagle0504/blip-vqa-finetuned-draft-1}
}

Model Card Authors

eagle0504 - Principal AI Engineer at FICO

Model Card Contact

https://huggingface.co/eagle0504