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eagle0504/blip-vqa-finetuned-draft-1

sourceHugging Faceupdated 1y agoView on Hugging Face
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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

python
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:

bibtex
@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