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View Code? Open in Web Editor NEWPytorch implementation of our WACV 2023 paper "Image-Consistent Detection of Road Anomalies As Unpredictable Patches"
Pytorch implementation of our WACV 2023 paper "Image-Consistent Detection of Road Anomalies As Unpredictable Patches"
Hi, both of these datasets have annotation available for road, anomaly and ignore regions. This is sufficient for evaluation of the anomaly detection methods. These datasets are not used for training, thus full semantic annotation is not required nor provided as far as I know. For further questions and details, I would suggest to contact the authors of the respective datasets.
Originally posted by @vojirt in #1 (comment)
Hello,
I am currently investigating the RoadAnomaly and Fishyscapes LAF datasets and would like to understand the data preprocessing steps involved. Since these datasets do not contain ground truth semantic segmentation maps, it's unclear how regions corresponding to roads are identified. I could not find details on the preprocessing methods in either the associated papers or the code.
Could you please provide some insights into how the data is prepared and processed for evaluation? I look forward to your response.
Thank you!
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