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## Dataset Information This dataset focuses on extracting pitting corrosion within Aluminum (Al) wires using in-situ XCT images acquired at Sandia National Laboratories (SNL). Currently, the dataset consists of 100 annotated images. - Images and corresponding groud truth masks are [here][1]. **Metadata:** - **Imaging technique:** XCT - **Sample:** Commercial-purity Aluminum (1100 Al) wire - **Diameter:** 0.813 mm - **Length:** 1.25 mm - **Sample exposure:** - **NaCl droplets:** 60 µg/cm² NaCl - **Relative Humidity:** 98% - **Image size:** 1024 x 988 - **Voxel size:** 1.25 µm - **Minimum resolvable feature size:** 15.6 µm² (2x2x2 voxels) **Want to contribute? Fill up this [Google Form][2]**. <h2> Benchmarks </h2> | Model | Description | Binary IoU | Library | Input Image Size | Performance Metrics | Paper | Evaluation Script | |:---------------------------------:|:--------------------------------------------:|:----------:|:--------------------:|:----------------:|:------------------------------------------:|:------------------------------------------:|:------------------------------------------:| [unet-seresnext101.h5][3]| U-Net with SE-ResNeXt101 Encoder Backbone | 79.2%|Tensorflow/Keras <br> ([Segmentation Models][4]) | 768 x 768| Accuracy <br> Precision <br> Recall <br> Binary IoU* | - | [Click Here][5] | |[unet-standard.h5][6]| U-Net model with 4-block encoder and decoder| 64.3% |Tensorflow/Keras | 768 x 768| Accuracy <br> Precision <br> Recall <br> Binary IoU*| - | [Click Here][7] | [1]: https://osf.io/uzxpc/files/osfstorage [2]: https://forms.gle/4pGRhkdw4wGNy1dY9 [3]: https://osf.io/download/7mqh8/ [4]: https://github.com/qubvel/segmentation_models [5]: https://osf.io/download/xc6gf/ [6]: https://osf.io/download/auzts/ [7]: https://osf.io/download/hqzd7/
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