fmajer commited on
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8dedea0
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1 Parent(s): 11f75d6

title change

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  1. README.md +2 -2
  2. app.py +2 -2
README.md CHANGED
@@ -1,13 +1,13 @@
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  ---
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  title: T-BOD
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- emoji: 👁
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  colorFrom: indigo
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  colorTo: purple
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  sdk: gradio
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  sdk_version: 3.29.0
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  app_file: app.py
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  pinned: false
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- license: mit
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  ---
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  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
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  ---
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  title: T-BOD
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+ emoji: 🤖
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  colorFrom: indigo
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  colorTo: purple
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  sdk: gradio
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  sdk_version: 3.29.0
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  app_file: app.py
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  pinned: false
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+ license: unknown
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  ---
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  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py CHANGED
@@ -57,7 +57,7 @@ def query_image(input_img, query, binarize, eval_threshold, crop_mode, crop_pct)
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  # Gradio interface
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  description = """
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- Gradio demo for an object detection architecture, introduced in my <a href="https://www.google.com/">bachelor thesis</a> (link will be added).
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  \n\n
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  You can use this architecture to detect objects using textual queries. To use it, simply upload an image and enter any query you want.
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  It can be a single word or a sentence. The model is trained to recognize only 80 categories from the COCO Detection 2017 dataset.
@@ -107,7 +107,7 @@ demo = gr.Interface(
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  gr.Radio(["center", "squash", "border"], value='center', label='crop_mode'), gr.Slider(0.7, 1, value=0.9, step=0.01)],
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  #outputs="image",
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  outputs=gr.Image(type='numpy', label='output').style(height=600, width=600),
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- title="Text-based Object Detection",
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  description=description,
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  examples=[
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  ["examples/img1.jpeg", "Find a person.", True, 0.45],
 
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  # Gradio interface
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  description = """
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+ Gradio demo for an object detection architecture, introduced in my bachelor thesis (link will be added).
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  \n\n
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  You can use this architecture to detect objects using textual queries. To use it, simply upload an image and enter any query you want.
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  It can be a single word or a sentence. The model is trained to recognize only 80 categories from the COCO Detection 2017 dataset.
 
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  gr.Radio(["center", "squash", "border"], value='center', label='crop_mode'), gr.Slider(0.7, 1, value=0.9, step=0.01)],
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  #outputs="image",
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  outputs=gr.Image(type='numpy', label='output').style(height=600, width=600),
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+ title="Text-Based Object Detection",
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  description=description,
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  examples=[
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  ["examples/img1.jpeg", "Find a person.", True, 0.45],