AliFadel/blip-cxr-mimic-finetuned

BLIP — Chest X-Ray Captioning (MIMIC-CXR Fine-Tune)

A fine-tuned version of Salesforce/blip-image-captioning-base on the MIMIC-CXR dataset for automatic chest X-ray report generation.

Dataset

• Source: itsanmolgupta/mimic-cxr-dataset
• Train split: 19,600 samples (indices 9,000 – 28,600)
• Validation split: 2,000 samples (indices 28,600 – 30,600)

Usage

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

repo_id  = "AliFadel/blip-cxr-mimic-finetuned"
processor = AutoProcessor.from_pretrained(repo_id)
model     = AutoModelForImageTextToText.from_pretrained(repo_id)
model.eval()

image = Image.open("chest_xray.jpg").convert("RGB")
inputs = processor(images=image, text="Chest X-Ray", return_tensors="pt")

with torch.no_grad():
    ids = model.generate(
        **inputs,
        num_beams=5,
        max_length=150,
        repetition_penalty=1.5,
        top_p=0.95,
    )

caption = processor.batch_decode(ids, skip_special_tokens=True)[0]
print(caption)

Intended Use

This model is intended for research purposes only. It should not be used as a clinical diagnostic tool without expert medical supervision.