Data from Automated plankton image analysis using convolutional neural networks

<p>Datasets and code from Luo et al., &quot;Automated plankton image analysis using convolutional neural networks.&quot; Limnology and Oceanography Methods.</p> <p>Data include:</p> <p>1) 42,564 item training library, sorted in 108 classes,</p> <p>2) 42,548 item test set for filtering thresholds, sorted into 38 groups. These images are independent from the training library, and are used for setting the thresholds for post-classification filtering.<br> CSV file:&nbsp;Luo_etal_FT_images_pred.csv&nbsp;contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p>3) 75,000 item fully random, validated set for confusion matrix calculations, sorted into 38 groups. This set is a representation of the full dataset, selected at random after classification.&nbsp;<br> CSV file:&nbsp;Luo_etal_confusionmatrix_images.csv&nbsp;contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p>&nbsp;</p> <p>Scripts and programs:</p> <p>1) Segmentation.zip contains the scripts and executables for the segmentation program.</p> <p>2) Plankton_template.zip contains the archived version of the SparseConvNet program used in manuscript&nbsp;(current version available at:&nbsp;https://github.com/btgraham/SparseConvNet or&nbsp;https://github.com/facebookresearch/SparseConvNet)<br> Note that google-sparsehash is necessary for running SparseConvNet.<br> Also,&nbsp;plankton_epoch-150.cnn are the weights from the training used in the manuscript, and should be placed in the /weights folder if you want&nbsp;to replicate the classifications.</p>