Ananta025/blip-ipvqa-final

BLIP-IPVQA (Agricultural Visual Question Answering)

This model is a fine-tuned version of Salesforce/blip-vqa-base trained on the Agri-VQA Plus (IPVQA) dataset.

The model answers natural language questions about agricultural images, such as pest identification, crop diseases, and field conditions.

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Dataset

Agri-VQA Plus (IPVQA)

• Image–Question–Answer pairs
• Agricultural domain (pests, crops, diseases)
• Optional image descriptions used during training

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Training Details

• Base Model: Salesforce/blip-vqa-base
• Framework: PyTorch + Hugging Face Transformers
• GPU: NVIDIA P100 (Kaggle)
• Epochs: 3
• Optimizer: AdamW
• Learning Rate: 5e-5
• Mixed Precision Training (AMP)
• Gradient Accumulation used for memory efficiency

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How to Use

from transformers import BlipProcessor, BlipForQuestionAnswering
from PIL import Image
import torch

processor = BlipProcessor.from_pretrained("Ananta025/blip-ipvqa-final")
model = BlipForQuestionAnswering.from_pretrained("Ananta025/blip-ipvqa-final")

image = Image.open("sample.jpg").convert("RGB")
question = "What pest is shown in the image?"

inputs = processor(image, question, return_tensors="pt")

with torch.no_grad():
    output = model.generate(**inputs)

answer = processor.decode(output[0], skip_special_tokens=True)
print(answer)