adirik commited on
Commit
fbfcc0e
β€’
1 Parent(s): ecb7e39
.DS_Store ADDED
Binary file (6.15 kB). View file
.gitattributes CHANGED
@@ -29,3 +29,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ assets/Helvatica.ttc filter=lfs diff=lfs merge=lfs -text
app.py CHANGED
@@ -13,14 +13,14 @@ def query_image(img, text_queries):
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  inputs = processor(text=text_queries, images=img, return_tensors="pt")
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  with torch.no_grad():
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- outputs = model(**inputs)
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  target_sizes = torch.Tensor([[768, 768]])
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  results = processor.post_process(outputs=outputs, target_sizes=target_sizes)
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  boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]
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  draw = ImageDraw.Draw(img)
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- font = ImageFont.truetype("/System/Library/Fonts/Helvetica.ttc", size=22)
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  score_threshold = 0.1
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  for box, score, label in zip(boxes, scores, labels):
@@ -35,19 +35,20 @@ def query_image(img, text_queries):
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  return img
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- description = description = """
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  Gradio demo for <a href="https://huggingface.co/docs/transformers/main/en/model_doc/owlvit">OWL-ViT</a>,
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  introduced in <a href="https://arxiv.org/abs/2205.06230">Simple Open-Vocabulary Object Detection
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  with Vision Transformers</a>.
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  \n\nYou can use OWL-ViT to query images with text descriptions of any object.
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  To use it, simply upload an image and enter comma separated text descriptions of objects you want to query the image for.
 
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  """
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  demo = gr.Interface(
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  query_image,
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  inputs=[gr.Image(shape=(768, 768), type="pil"), "text"],
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  outputs="image",
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  title="Zero-Shot Object Detection with OWL-ViT",
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- description="You can use OWL-ViT to query images with text descriptions of any object",
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- examples=[["astronaut.png", "human face, rocket, flag, nasa badge"], ["coffee.png", "coffee mug, spoon, plate"]]
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  )
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  demo.launch(debug=True)
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  inputs = processor(text=text_queries, images=img, return_tensors="pt")
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  with torch.no_grad():
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+ outputs = model(**inputs)
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  target_sizes = torch.Tensor([[768, 768]])
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  results = processor.post_process(outputs=outputs, target_sizes=target_sizes)
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  boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]
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  draw = ImageDraw.Draw(img)
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+ font = ImageFont.truetype("assets/Helvetica.ttc", size=22)
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  score_threshold = 0.1
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  for box, score, label in zip(boxes, scores, labels):
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  return img
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+ description = """
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  Gradio demo for <a href="https://huggingface.co/docs/transformers/main/en/model_doc/owlvit">OWL-ViT</a>,
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  introduced in <a href="https://arxiv.org/abs/2205.06230">Simple Open-Vocabulary Object Detection
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  with Vision Transformers</a>.
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  \n\nYou can use OWL-ViT to query images with text descriptions of any object.
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  To use it, simply upload an image and enter comma separated text descriptions of objects you want to query the image for.
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+ \n\n<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb">Colab demo</a>
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  """
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  demo = gr.Interface(
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  query_image,
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  inputs=[gr.Image(shape=(768, 768), type="pil"), "text"],
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  outputs="image",
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  title="Zero-Shot Object Detection with OWL-ViT",
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+ description=description,
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+ examples=[["assets/astronaut.png", "human face, rocket, flag, nasa badge"], ["assets/coffee.png", "coffee mug, spoon, plate"]]
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  )
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  demo.launch(debug=True)
assets/.DS_Store ADDED
Binary file (6.15 kB). View file
assets/Helvatica.ttc ADDED
@@ -0,0 +1,3 @@
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:c4cb0b7e8b3e53b29d356bfae0663075fdd20e45ad94b56e1b77cca117e415e0
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+ size 2356684
astronaut.png β†’ assets/astronaut.png RENAMED
File without changes
coffee.png β†’ assets/coffee.png RENAMED
File without changes