Training Data and Models for the paper: Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM Images of Steel Alloys

This dataset contains scanning electron microscopy (SEM) images of steel alloys, including paired secondary electron (SE2) and in-lens (InLens) channels, with corresponding binary segmentation labels. The data supports full reproduction of results presented in the referenced manuscript. <strong>Dataset Description</strong> <strong>Content:</strong> 13 pairs of SEM images of two reactor pressure vessel (RPV) steels: <em>JFL</em>: IAEA reference RPV base metal steel <em>ANP-10</em>: Western type RPV steel <strong>Acquisition:</strong> <em>JFL</em>: Zeiss NVision 40 microscope <em>ANP-10</em>: Zeiss Ultra 55 microscope Both SE and InLens detectors used simultaneously. <strong>Resolution:</strong> 2048 × 1404 pixels per image 2048 px width corresponds to 14.3 µm (JFL) or 11.5 µm (ANP-10). Using the dataset to reproduce the results of the manuscript Download the zip file into the <code>data/</code> subdirectory of the code repository and extract the archive: <pre><code class="language-bash">cd data/ unzip data.zip</code></pre> <strong>Dataset Structure</strong> These directories contain the relevant data for the manuscript: <code>cloud/</code><br> <code>├-─ preprocessed/</code><br> <code>│ ├── hold-out/</code><br> <code>│ ├── images/</code><br> <code>│ └── labels/</code><br> <code>├── processed_tiles/</code><br> <code>│ ├── images/</code><br> <code>│ └── labels/</code><br> <code>├── tb_logs/</code><br> <code>│ ├── unet_model/</code> <strong>Preprocessed</strong> pre-processed whole images and corresponding labels <strong>Processed Tiles</strong> tiled images and labels <strong>tb_logs</strong> trained model weights