Model Card: ClipMD
Model Details
ClipMD is a medical image-text matching model based on OpenAI's CLIP model with a sliding window text encoder.
Model Description
The model uses a ViT-B/32 Transformer architecture as an image encoder and uses a masked sliding window elf-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.
The model was fine-tuned on the ROCO dataset.
Use with Transformers
from PIL import Image
from transformers import AutoProcessor,AutoModel
model = AutoModel.from_pretrained("Idan0405/ClipMD",trust_remote_code=True)
processor = AutoProcessor.from_pretrained("Idan0405/ClipMD")
image = Image.open("your image path")
inputs = processor(text=["chest x-ray", "head MRI"], images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs[0] # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities