Comments (2)
I'll try to repeat your questions to make sure I understand what you're asking.
- How do I generate the prediction image in the visualization? We simply feed in the image and ask the model to predict which of the in-distribution classes is present for each pixel.
- How do we select a threshold for plotting whether or not something is anomaly? We plot the ROC or PR curves and based on what target precision you're aiming for you can select theta based on that.
- How do we plot the anomaly scores? Let's take MaxLogit as an example. For each pixel after being passed through the model you obtain an array of predictions. Select the max among that array. You now have 1 value per pixel. This can be plotted on grey scale or we can use the viridis color scheme as we chose to do as it is more visually pleasing to readers.
Hope that answers all of your questions. I will close this issue tomorrow if there are no follow-up questions :)
from anomaly-seg.
You solved all my questions,thank you very much for your answer.
from anomaly-seg.
Related Issues (20)
- Question: Paper Section 3 HOT 4
- pretrained model HOT 4
- Issues with evaluate the model on OOD test set HOT 1
- Couldn't match the BDD100K file paths HOT 3
- StreetHazards color encoding for visualization HOT 6
- Some questions about the CAOS benchmark HOT 2
- Some points to confirm about the CAOS benchmark HOT 4
- Two more stages in resnet HOT 2
- how about the class define? HOT 2
- how to show the anomaly scores? HOT 1
- Evaluation based on each image vs entire dataset HOT 1
- What is the true label of -1 in seg_label HOT 1
- Training Error?? HOT 7
- About metrics AUPR, AUROC, and FPR95 HOT 2
- About ground truth Semantic Segmentation images HOT 4
- Broken link to BDD-Anomaly dataset HOT 1
- Updating the google drive link of the Species OOD dataset HOT 4
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from anomaly-seg.