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import gradio as gr | |
from demo import query_image | |
description = """ | |
Gradio demo for <a href="https://huggingface.co/docs/transformers/main/en/model_doc/owlvit">OWL-ViT</a>, | |
introduced in <a href="https://arxiv.org/abs/2205.06230">Simple Open-Vocabulary Object Detection | |
with Vision Transformers</a>. | |
\n\nYou can use OWL-ViT to query images with text descriptions of any object. | |
To use it, simply upload an image and enter comma separated text descriptions of objects you want to query the image for. You | |
can also use the score threshold slider to set a threshold to filter out low probability predictions. | |
\n\nOWL-ViT is trained on text templates, | |
hence you can get better predictions by querying the image with text templates used in training the original model: *"photo of a star-spangled banner"*, | |
*"image of a shoe"*. Refer to the <a href="https://arxiv.org/abs/2103.00020">CLIP</a> paper to see the full list of text templates used to augment the training data. | |
\n\n<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb">Colab demo</a> | |
""" | |
demo = gr.Interface( | |
query_image, | |
inputs=[gr.Image(), "text"], | |
outputs=["image", "image"], | |
title="Zero-Shot Object Detection with OWL-ViT", | |
description=description, | |
examples=[ | |
["./demo_images/cats.png", "cats,ears"], | |
["./demo_images/demo1.jpg", "bear,soil,sea"], | |
["./demo_images/demo2.jpg", "dog,ear,leg,eyes,tail"], | |
["./demo_images/tanager.jpg", "wing,eyes,back,legs,tail"] | |
], | |
) | |
# demo.launch() | |
demo.launch(server_name="0.0.0.0", debug=True) |