examples
Browse files- app.py +1 -6
- examples/IMGP0178.jpg +0 -0
app.py
CHANGED
|
@@ -55,13 +55,8 @@ def query_image(img, text_queries, score_threshold):
|
|
| 55 |
|
| 56 |
description = """
|
| 57 |
\n\nYou can use OWL-ViT to query images with text descriptions of any object.
|
| 58 |
-
To use it, simply
|
| 59 |
can also use the score threshold slider to set a threshold to filter out low probability predictions.
|
| 60 |
-
|
| 61 |
-
\n\nOWL-ViT is trained on text templates,
|
| 62 |
-
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"*,
|
| 63 |
-
*"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.
|
| 64 |
-
\n\n<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb">Colab demo</a>
|
| 65 |
"""
|
| 66 |
demo = gr.Interface(
|
| 67 |
query_image,
|
|
|
|
| 55 |
|
| 56 |
description = """
|
| 57 |
\n\nYou can use OWL-ViT to query images with text descriptions of any object.
|
| 58 |
+
To use it, simply upload an image and enter comma separated text descriptions of objects you want to query the image for. You
|
| 59 |
can also use the score threshold slider to set a threshold to filter out low probability predictions.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 60 |
"""
|
| 61 |
demo = gr.Interface(
|
| 62 |
query_image,
|
examples/IMGP0178.jpg
ADDED
|