Blip Cxr Mimic Finetuned

--- language: - en license: apache-2.0 tags: - medical - radiology - chest-xray - image-captioning - blip - mimic-cxr datasets: - itsanmolgupta/mimic-cxr-dataset base_model: Salesforce/blip-image-captioning-base model_type: blip pipeline_tag: image-to-text library_name: transformers --- # BLIP — Chest X-Ray Captioning (MIMIC-CXR Fine-Tune) A fine-tuned version of [`Salesforce/blip-image-captioning-base`](https://huggingface.co/Salesforce/blip-image-captioning-base) on the **MIMIC-CXR** dataset for automatic chest X-ray report generation. ## Dataset - **Source**: [`itsanmolgupta/mimic-cxr-dataset`](https://huggingface.co/datasets/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 ```python 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.

Source context: 14 downloads · 1 likes · Pipeline image-to-text · Library transformers · Repo AliFadel/blip-cxr-mimic-finetuned

Source context: 18 downloads · 1 likes · Pipeline image-to-text · Library transformers · Repo AliFadel/blip-cxr-mimic-finetuned