| --- |
| 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 |
| --- |
| # RobustAD Dataset |
| ## 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. |
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|
|
| ## Dataset Card for RobustAD |
|
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| For more details, refer to this paper: COMING SOON! |
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|
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| ## How to Use |
|
|
| To load the dataset, |
|
|
| ``` |
| from datasets import load_dataset |
| from datasets import Image |
| |
| #For piled bags dataset (Classification only) |
| piled_bags_dataset = load_dataset("imagefolder", data_files={"train": 'PiledBags/piled_bags_data_dir_train/*', "test0": 'PiledBags/piled_bags_data_dir_test0/*' , "test1": 'PiledBags/piled_bags_data_dir_test1/*' , "test2": 'PiledBags/piled_bags_data_dir_test2/*' , "test3": 'PiledBags/piled_bags_data_dir_test3/*' ,"test4": 'PiledBags/piled_bags_data_dir_test4/*', "test5": 'PiledBags/piled_bags_data_dir_test5/*'}) |
| |
| #For PCB dataset |
| pcb_dataset = load_dataset("imagefolder", data_files={"train": 'PCB/pcb_data_dir_train/*', "test0": 'PCB/pcb_data_dir_test0/*', "test1": 'PCB/pcb_data_dir_test1/*' , "test2": 'PCB/pcb_data_dir_test2/*' , "test3": 'PCB/pcb_data_dir_test3/*' ,"test4": 'PCB/pcb_data_dir_test4/*', "test5": 'PCB/pcb_data_dir_test5/*'}).cast_column("mask", Image(decode=True)) |
| |
| #For Metal Parts dataset |
| metal_parts_dataset = load_dataset("imagefolder", data_files={"train": 'MetalParts/metal_parts_data_dir_train/*', "test0": 'MetalParts/metal_parts_data_dir_test0/*' , "test1": 'MetalParts/metal_parts_data_dir_test1/*' , "test2": 'MetalParts/metal_parts_data_dir_test2/*' , "test3": 'MetalParts/metal_parts_data_dir_test3/*' ,"test4": 'MetalParts/metal_parts_data_dir_test4/*', "test5": 'MetalParts/metal_parts_data_dir_test5/*', "test6": 'MetalParts/metal_parts_data_dir_test6/*'}).cast_column("mask", Image(decode=True)) |
| |
| #metal_parts_dataset['train'][0] - Normal sample does not have a mask |
| #{'image': <PIL.Image.Image image mode=RGB size=2681x1500 at 0x7F66A1BE46D0>, 'label': 0, 'mask': None} |
| |
| #metal_parts_dataset['train'][0] - Anomaly samples have a mask |
| {'image': <PIL.Image.Image image mode=RGB size=2681x1500 at 0x7F66A1B1EBC0>, 'label': 1, 'mask': <PIL.PngImagePlugin.PngImageFile image mode=L size=2681x1500 at 0x7F66A1BE7040>} |
| |
| ``` |
|
|
| ## License Information |
|
|
| The RobustAD dataset is released under the Creative Commons license cc-by-4.0. |
|
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| ## Citation Information |
|
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| COMING SOON! |
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|
|
| ## Contact |
|
|
| lppemula@amazon.com (Latha Pemula) | zdongqin@amazon.com (Dongqing Zhang) | onkardab@amazon.com (Onkar Dabeer) |
