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CameraTrapDetectoR Pig Model

    This dataset contains the model weights, architecture, and class label dictionary for CameraTrapDetectoR pig-only model version 1. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. This model identifies and counts wild pigs in camera trap images. It also includes categories for non-pig detections (animal, human, or vehicle), and a background class.

    CameraTrapDetectoR General Model

      This dataset contains the model weights, architecture, and class label dictionary for CameraTrapDetectoR general model version 1. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. This model identifies and counts mammals and birds in camera trap images, and includes categories for humans, vehicles, and a background class.

      CameraTrapDetectoR Family Model

        This dataset contains the model weights, architecture, and class label dictionary for CameraTrapDetectoR family model version 1. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. This model identifies and counts animals from 33 North American taxonomic families in camera trap images, including humans vehicles and a background class.

        CameraTrapDetectoR Species Model

          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 dataset contains model weights, architectures, and class label dictionaries for the CameraTrapDetectoR species models.

          SNAPMe: A Benchmark Dataset of Food Photos with Food Records for Evaluation of Computer Vision Algorithms in the Context of Dietary Assessment

            We conducted the Surveying Nutrient Assessment with Photographs of Meals (SNAPMe) Study (ClinicalTrials ID: NCT05008653) to develop a benchmark dataset of food photographs paired with traditional food records. The SNAPMe DB includes 1,475 “before” photos of non-packaged foods, 1,436 “after” photos of non-packaged foods, 203 “front” photos of packaged foods, and 196 “ingredient” labels of packaged foods. Each line item of each ASA24 food record is linked to the relevant photo. These data will be transformative for the improvement of artificial intelligence algorithms for the adoption of photo-based dietary assessment in nutrition research.

            WIC Infant and Toddler Feeding Practices Study-2 (WIC ITFPS-2): Prenatal, Infant Year, Second Year, Third Year, and Fourth Year Datasets

              The WIC Infant and Toddler Feeding Practices Study–2 (WIC ITFPS-2) addresses a series of research questions regarding feeding practices, the effect of WIC services on those practices, and the health and nutrition outcomes of children on WIC. These datasets include data from caregivers and their children during the prenatal period and during the children’s first four years of life (child ages 1 to 48 months).

              Reynolds Creek Experimental Watershed, Idaho (Sediment)

                Automated Sigma pump samplers were used at all RCEW gauging stations to collect instantaneous point measures of suspended-sediment concentration. The US Department of Agriculture, Agricultural Research Service, Northwest Watershed Research Center initiated a stream discharge and suspended-sediment research program at Reynolds Creek Experimental Watershed in the early 1960s. Samples of suspended-sediment concentration were collected at Outlet, Tollgate, and Reynolds Mountain East gauging stations starting in the 1960s and continuing to the present.