hlydecker commited on
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06c6706
1 Parent(s): 09a96ae

update: changed example picture

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  1. app.py +1 -1
app.py CHANGED
@@ -28,5 +28,5 @@ title = "Are you wearing a mask?"
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  description = "Detecting masked and unmasked faces with YOLOv5. Take a picture, upload an image, or click an example image to use."
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  article = "<p style='text-align: center'>This app makes predictions using a YOLOv5s model that was fine tuned on a dataset of people with and without masks. All of the code for training the model is available on <a href='https://github.com/hlydecker/are-you-wearing-a-mask'>GitHub</a>. This app and the model behind it were created by Henry Lydecker, as part of his work at the Sydney Informatics Hub, a Core Research Facility of The University of Sydney. Find out more about the YOLO model from the original creator, <a href='https://pjreddie.com/darknet/yolo/'>Joseph Redmon</a>. Here's the Ultralytics YOLOv5 blurb: YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
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- examples = [['data/picard.jpg'], ['data/stockmasks.jpg'],['data/batman.png']]
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  gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(enable_queue=True)
 
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  description = "Detecting masked and unmasked faces with YOLOv5. Take a picture, upload an image, or click an example image to use."
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  article = "<p style='text-align: center'>This app makes predictions using a YOLOv5s model that was fine tuned on a dataset of people with and without masks. All of the code for training the model is available on <a href='https://github.com/hlydecker/are-you-wearing-a-mask'>GitHub</a>. This app and the model behind it were created by Henry Lydecker, as part of his work at the Sydney Informatics Hub, a Core Research Facility of The University of Sydney. Find out more about the YOLO model from the original creator, <a href='https://pjreddie.com/darknet/yolo/'>Joseph Redmon</a>. Here's the Ultralytics YOLOv5 blurb: YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
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+ examples = [['data/picard.jpg'], ['data/stockmasks.jpg'],['data/bts.png']]
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  gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(enable_queue=True)