Insect benchmark datasets for training, validation and test (train1201.zip, val1201.zip and test1201.zip) with time-lapse images as described in paper: Bjerge K, Alison J, Dyrmann M, Frigaard C.E., Mann H. M. R., Høye T.T., Accurate detection and identification of insects from camera trap images with deep learning, bioRxiv:10.1101/2022.10.25.513484v1 Labels in YOLO format: ultralytics/yolov5: label format The annotated training and validation datasets contains insects of nine different species as listed below: 0 Coccinellidae septempunctata 1 Apis mellifera 2 Bombus lapidarius 3 Bombus terrestris 4 Eupeodes corolla 5 Episyrphus balteatus 6 Aglais urticae 7 Vespula vulgaris 8 Eristalis tenax The test dataset contains additional classes of insects. 9 Non-Bombus Anthophila 10 Bombus spp. 11 Syrphidae 12 Fly spp. 13 Unclear insect 14 Mixed animals: —————————— Rhopalocera Non-Anthophila Hymenoptera Non-Syrphidae Diptera Non-Conccinalidae Coleoptera Concinellidae Other animals There are two naming conventions for image (.jpg) and label (.txt) files. Background images without insects are named: “X_Seq-YYYYMMDDHHMMSS-snapshot”. E.g.: Background image: 12_13-20190704172200-snapshot.jpg Empty label file: 12_13-20190704172200-snapshot.txt Images annotated with insects are named: “SZ_IP-MonthDate_C_Seq-YYYYMMDDHHMMSS”. E.g.: Image file: S1_146-Aug23_1_156-20190822133230.jpg Label file: S1_146-Aug23_1_156-20190822133230.txt Abbreviations: YYYYMMDDHHMMSS – Capture timestamp with year, month, date, hour, minutes, and second Seq – Sequence number created by the motion program to separate images C – Identification of two cameras with Id=0 or Id=1 in system identified by SZ_IP MonthDate – Folder name for where the original image were stored in the system SZ_IP – Identification of five camera systems: S1_123, S2_146, S3_194, S4_199, S5_187 (Two cameras in each system) X – An index number related to a specific camera and folder ensuring unique file names of background images from different camera systems. The important information in a filename is system (SZ_IP), camera Id (C) and timestamp (YYYYMMDDHHMMSS). The three best YOLOv5 models (YOLOv5models.zip) from the paper are available in pytorch format. All models are tested with YOLOv5 release v7.0 (22-11-2022): ultralytics/yolov5: YOLOv5 in PyTorch insect1201-bestF1-640v5m.pt: Model no. 6 in Table 2 (F1=0.912) insect1201-bestF1-1280v5m6.pt: Model no. 8 in Table 2 (F1=0.925) insect1201-bestF1-1280v5m6.pt: Model no. 10 in Table 2 (F1=0.932) insects-1201val.yaml: YAML file with label names to train YOLOv5 trainInsects-1201m.sh: Linux bash shell script with parameters to train YOLOv5m6 valInsectsF1-1201.sh: Linux bash shell script with parameters to validated models

{"references": ["Bjerge K, Alison J, Dyrmann M, Frigaard C.E., Mann H. M. R., H\u00f8ye T.T., Accurate detection and identification of insects from camera trap images with deep learning, bioRxiv:10.1101/2022.10.25.513484v1"]}