PhenoLearn: a user-friendly toolkit for image annotation and deep learning-based phenotyping for biological datasets.

The digitization of natural history specimens has unlocked opportunities for large-scale phenotypic trait analysis. In recent years, deep learning has shown significant results in accurately predicting annotations on 2D specimen photographs. However, it can be challenging for biologists without extensive related expertise to easily use deep learning. Here, we introduce PhenoLearn, a toolkit developed for biologists to generate annotations on 2D specimen images using deep learning. PhenoLearn integrates graphical user interfaces (GUIs) within its two main modules, PhenoLabel for image annotation and PhenoTrain for model training and prediction. GUIs increase accessibility and reduce the need for computational expertise, allowing biologists to intuitively go through a workflow of labelling training sets, using deep learning, and reviewing predictions in the same tool. We demonstrate PhenoLearn's capabilities through a case study involving the segmentation of plumage areas on bird images, showcasing prediction accuracy and the running time with and without graphics processing unit, highlighting its potential to generate annotations with minimal computational cost and time. The toolkit's modular design and flexibility ensure adaptability, allowing for integration with other tools amidst rapidly evolving deep learning approaches. PhenoLearn bridges the gap between specimen digitization and downstream analysis, providing biologists with broader access to deep learning. The source code, installation guides, tutorials with screenshots, and a small demo dataset for PhenoLearn can be found at https://github.com/echanhe/phenolearn.