Camera trapping has become increasingly common in ecological studies, but is hindered by analyzing large datasets. Recently, artificial intelligence (deep learning models in particular) has emerged as a promising solution. However, applying deep learning for images processing is complex and often requires programming skills in Python, reducing its accessibility. Some authors addressed this issue with user‐friendly software, and a further progress was the transposition of deep learning to R, a statistical language frequently used by ecologists, enhancing flexibility and customization of deep learning models without advanced computer expertise. We aimed to develop a user‐friendly workflow based on R scripts to streamline the entire process, from selecting to classifying camera trap images. Our workflow integrates the MegaDetector object detector for labelling images and custom training of the state‐of‐the‐art YOLOv8 model, together with potential for offline image augmentation to manage imbalanced datasets. Inference results are stored in a database compatible with Timelapse for quality checking of model predictions. We tested our workflow on images collected within a project targeting medium and large mammals of Central Italy, and obtained an overall precision of 0.962, a recall of 0.945, and a mean average precision of 0.913 for a training set of only 1000 pictures per species. Furthermore, the custom model achieved 91.8% of correct species‐level classifications on a set of unclassified images, reaching 97.1% for those classified with > 90% confidence. YOLO, a fast and light deep learning architecture, enables application of the workflow even on resource‐limited machines, and integration with image augmentation makes it useful even during early stages of data collection. All R scripts and pretrained models are available to enable adaptation of the workflow to other contexts, plus further development.
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