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monitoringObjectDetection

Runtime error

HugoHE commited on May 7, 2023

Commit

2be04db

1 Parent(s): 59dd5ea

Update app.py

Files changed (1) hide show

app.py CHANGED Viewed

@@ -175,7 +175,7 @@ def inference_gd(file):
 examples = ["examples/0.jpg", "examples/1.jpg", "examples/2.jpg", "examples/3.jpg"]
-with gr.Blocks(theme='gradio/monochrome') as demo:
     gr.Markdown("# Runtime Monitoring Object Detection")
     gr.Markdown(
         """This interactive demo is based on the box abstraction-based monitors for Faster R-CNN model. The model is trained using [Detectron2](https://github.com/facebookresearch/detectron2) library on the in-distribution dataset [Berkeley DeepDrive-100k](https://www.bdd100k.com/), which contains objects within autonomous driving domain. The monitors are constructed by abstraction of extracted feature from the training data. The demo showcases the monitors' capacity to reject problematic detections due to out-of-distribution(OOD) objects.

 examples = ["examples/0.jpg", "examples/1.jpg", "examples/2.jpg", "examples/3.jpg"]
+with gr.Blocks(theme="gradio/monochrome") as demo:
     gr.Markdown("# Runtime Monitoring Object Detection")
     gr.Markdown(
         """This interactive demo is based on the box abstraction-based monitors for Faster R-CNN model. The model is trained using [Detectron2](https://github.com/facebookresearch/detectron2) library on the in-distribution dataset [Berkeley DeepDrive-100k](https://www.bdd100k.com/), which contains objects within autonomous driving domain. The monitors are constructed by abstraction of extracted feature from the training data. The demo showcases the monitors' capacity to reject problematic detections due to out-of-distribution(OOD) objects.