Jessica Walkenhorst commited on
Commit
586ec44
1 Parent(s): 827bdd4

Remove additional examples

Browse files
Files changed (4) hide show
  1. app.py +2 -2
  2. sunflower.jpg +0 -3
  3. whiteclouds.jpg +0 -3
  4. yellowclouds.jpg +0 -3
app.py CHANGED
@@ -14,9 +14,9 @@ def classify_image(image):
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  title = 'Mirror, Mirror on the Wall, am I a Duckling or a Cygnet after all?'
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  description = """Hans Christian Andersen's tale of the ugly duckling tells us about the sad youth of a cygnet which is accidentally brought up in a family of ducks and is ostrized on the account of it being different. But what if the cygnet had had a magic mirror to tell it that it had been a young swan all along? Machine learning to the rescue!"""
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- examples = ['duckling.jpg', 'cygnet.jpg', 'sunflower.jpg', 'whiteclouds.jpg', 'yellowclouds.jpg']
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- article = 'This model was build using a resnet-18 architecture with weights pretrained on the ImageNet data set and fine-tuned using about 80 images of ducklings and cygnets each.\nNote that it is binary classifier and can therefore only output cygnet or duckling, "other" is not an option. As a fun exercise, I included some non-waterfowl pictures in the example. Can you guess what the model will classify them as?\nOn a final note, whilst this classifier claims to be able to detect ducklings, it really only detects mallard ducklings (aka the yellow ones) and has a hard time recognizing ducklings of other species. To see this in action, compare its performance for a mallard duckling with its performance when given the image of a black cayuga duckling for example.'
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  app = gr.Interface(fn=classify_image,
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  inputs=gr.components.Image(),
 
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  title = 'Mirror, Mirror on the Wall, am I a Duckling or a Cygnet after all?'
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  description = """Hans Christian Andersen's tale of the ugly duckling tells us about the sad youth of a cygnet which is accidentally brought up in a family of ducks and is ostrized on the account of it being different. But what if the cygnet had had a magic mirror to tell it that it had been a young swan all along? Machine learning to the rescue!"""
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+ examples = ['duckling.jpg', 'cygnet.jpg']
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+ article = 'This model was build using a resnet-18 architecture with weights pretrained on the ImageNet data set and fine-tuned using about 80 images of ducklings and cygnets each.\nNote that it is binary classifier and can therefore only output cygnet or duckling, "other" is not an option.'
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  app = gr.Interface(fn=classify_image,
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  inputs=gr.components.Image(),
sunflower.jpg DELETED

Git LFS Details

  • SHA256: 7ec45bfe341e2aa2c155035a404322b278873beca5570e8ea8480b09a42ca923
  • Pointer size: 130 Bytes
  • Size of remote file: 54 kB
whiteclouds.jpg DELETED

Git LFS Details

  • SHA256: e9e02289c0396b284e8da15c7e8c37c3e011746aa9daf5275804c52007fb7d8a
  • Pointer size: 130 Bytes
  • Size of remote file: 16.3 kB
yellowclouds.jpg DELETED

Git LFS Details

  • SHA256: 4c8133c935517673ecfe13c1d467ecc620f841e42870dac3d12b0e63e0572523
  • Pointer size: 130 Bytes
  • Size of remote file: 19.7 kB