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flaviagiammarino
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pubmed-clip-vit-base-patch32

Zero-Shot Image Classification
Transformers
PyTorch
TensorFlow
JAX
English
clip
medical
vision
Inference Endpoints
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3
flaviagiammarino commited on
Commit
2b98077
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1 Parent(s): 525b1c2

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Files changed (2) hide show
  1. scripts/pt_example.py +4 -3
  2. scripts/tf_example.py +4 -3
scripts/pt_example.py CHANGED
@@ -7,15 +7,16 @@ from transformers import CLIPProcessor, CLIPModel
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  model = CLIPModel.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
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  processor = CLIPProcessor.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
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- url = "https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcSjP8UWzpGqXKwlC1zPRhcJOXThfI4pXgg2Zhd1B-cstpnEDalY"
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  image = Image.open(requests.get(url, stream=True).raw)
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- text = ["Chest X-Ray", "Brain MRI"]
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  inputs = processor(text=text, images=image, return_tensors="pt", padding=True)
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  probs = model(**inputs).logits_per_image.softmax(dim=1).detach().numpy().flatten()
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  plt.subplots()
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  plt.imshow(image)
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- plt.title("".join([x[0] + ": " + x[1] + " " for x in zip(text, [format(prob, ".4%") for prob in probs])]))
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  plt.axis("off")
 
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  plt.show()
 
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  model = CLIPModel.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
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  processor = CLIPProcessor.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
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+ url = "https://d168r5mdg5gtkq.cloudfront.net/medpix/img/full/synpic18755.jpg"
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  image = Image.open(requests.get(url, stream=True).raw)
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+ text = ["Chest X-Ray", "Brain MRI", "Abdominal CT Scan"]
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  inputs = processor(text=text, images=image, return_tensors="pt", padding=True)
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  probs = model(**inputs).logits_per_image.softmax(dim=1).detach().numpy().flatten()
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  plt.subplots()
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  plt.imshow(image)
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+ plt.title("".join([x[0] + ": " + x[1] + "\n" for x in zip(text, [format(prob, ".4%") for prob in probs])]))
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  plt.axis("off")
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+ plt.tight_layout()
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  plt.show()
scripts/tf_example.py CHANGED
@@ -8,15 +8,16 @@ from transformers import CLIPProcessor, TFCLIPModel
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  model = TFCLIPModel.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
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  processor = CLIPProcessor.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
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- url = "https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcSjP8UWzpGqXKwlC1zPRhcJOXThfI4pXgg2Zhd1B-cstpnEDalY"
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  image = Image.open(requests.get(url, stream=True).raw)
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- text = ["Chest X-Ray", "Brain MRI"]
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  inputs = processor(text=text, images=image, return_tensors="tf", padding=True)
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  probs = tf.nn.softmax(model(**inputs).logits_per_image, axis=-1).numpy().flatten()
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  plt.subplots()
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  plt.imshow(image)
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- plt.title("".join([x[0] + ": " + x[1] + " " for x in zip(text, [format(prob, ".4%") for prob in probs])]))
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  plt.axis("off")
 
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  plt.show()
 
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  model = TFCLIPModel.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
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  processor = CLIPProcessor.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
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+ url = "https://d168r5mdg5gtkq.cloudfront.net/medpix/img/full/synpic18755.jpg"
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  image = Image.open(requests.get(url, stream=True).raw)
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+ text = ["Chest X-Ray", "Brain MRI", "Abdominal CT Scan"]
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  inputs = processor(text=text, images=image, return_tensors="tf", padding=True)
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  probs = tf.nn.softmax(model(**inputs).logits_per_image, axis=-1).numpy().flatten()
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  plt.subplots()
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  plt.imshow(image)
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+ plt.title("".join([x[0] + ": " + x[1] + "\n" for x in zip(text, [format(prob, ".4%") for prob in probs])]))
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  plt.axis("off")
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+ plt.tight_layout()
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  plt.show()