| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - computer-vision |
| - anomaly-detection |
| - industrial |
| - defect-detection |
| pretty_name: 'RobustAD: A Realworld Anomaly Detection Dataset for Robustness ' |
| size_categories: |
| - 1K<n<10K |
| --- |
| #About the Dataset |
|
|
| RobustAD, specifically designed to evaluate the robustness of anomaly detection models in real-world scenarios. RobustAD features a curated dataset of defect detection images with meticulously controlled distribution shifts across multiple dimensions relevant to practical applications and more closely mirrors real-world deployment scenarios. |
| RobustAD is designed to cover inspection challenges across multiple industries to ensure the diversity of use cases and |
| encourage the development of generalizable methods. It is carefully curated to reflect the complexity of real-world |
| anomaly detection task in terms of both the defect variations and the domain shifts captured in the data. Robus- |
| tAD consists of 3 sub-datasets corresponding to 3 different objects of interest, each with a source domain data for |
| training and multiple target domains with different shifts for testing. |
| The PCB sub-dataset captures the challenges |
| of finding subtle scratches, soldering melts, and missing parts which comprise of the most common defects encoun- |
| tered during inspection of Printed Circuit Boards in electronics and semiconductor manufacturing. The metal parts |
| sub-dataset reflects the challenges of inspecting metal automotive parts with reflective surfaces for possible chipping, |
| dents, or porosity (holes in metal) in the automotive industry. The pile of packets represents a common count-based |
| anomaly detection task performed by packaging machines in the pharmaceutical industry. We believe this broad cov- |
| erage of tasks and anomaly types across important sectors ensures a general model that is relevant for common in- |
| dustry inspection problems and serves as a good starting point. The PCB and metal parts datasets are defined for |
| localization and classification tasks where as piled packets subset is only defined for classification task. |
|
|
|
|
|
|
| #Dataset Card for RobustAD |
|
|
| For more details, refer to this paper: COMING SOON! |
| The three sub-datasets and the defects covered in each sub-dataset are listed below |
|
|
|
|
|
|
| #How to Use |
| TBD |
|
|
| #License Information |
|
|
| The RobustAD dataset is released under the Creative Commons license cc-by-4.0. |
|
|
| #Citation Information |
|
|
| COMING SOON! |
|
|
|
|
| #Contact |
|
|
| lppemula@amazon.com (Latha Pemula) | zdongqin@amazon.com (Dongqing Zhang) | onkardab@amazon.com (Onkar Dabeer) |
