|
--- |
|
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. |
|
|
|
|
|
|
|
## Dataset Card for RobustAD |
|
|
|
For more details, refer to this paper: COMING SOON! |
|
|
|
|
|
|
|
|
|
## 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. |
|
|
|
## Citation Information |
|
|
|
COMING SOON! |
|
|
|
|
|
## Contact |
|
|
|
[email protected] (Latha Pemula) | [email protected] (Dongqing Zhang) | [email protected] (Onkar Dabeer) |
