CameraTrapDetectoR is an R package that uses deep learning computer vision models to automatically detect, count, and classify common North American domestic and wild species in camera trap images. Data for all versions of the taxonomic species model are located in this dataset. This data is automatically downloaded, extracted, and deployed in the tool's deploy_model function. Additional information about the R package and the training data can be found in the package's Github repository: https://github.com/CameraTrapDetectoR/CameraTrapDetectoR

This research used resources provided by the SCINet project and the AI Center of Excellence of the USDA Agricultural Research Service, ARS project number 0500-00093-001-00-D.

List of Resources:

• species_v1.zip is a folder containing the model weights, model architecture, and class label dictionary for the first version of the species model. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone.
• species_v2.zip is a folder containing the model weights, model architecture, and class label dictionary for the second version of the species model. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone, trained on the ARS SCINet Atlas cluster. This model identifies and counts 78 North American species in camera trap images, including humans vehicles and a background class. The training dataset contains 169,352 unique images, with an average of 2199 images per class excluding background class. The (min, max) range of images count per class is (107, 7027); this class imbalance was addressed with a suite of data augmentations and weighted random sampling. Images were acquired from a total of 26 databases across North America.