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Benchmark datasets for detection and identification of insects from camera trap images with deep learning

Insect benchmark datasets for training, validation and test (train1201.zip, val1201.zip and test1201.zip) with time-lapse images as described in paper: <a href="https://www.biorxiv.org/content/10.1101/2022.10.25.513484v1">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</a> Labels in YOLO format: <a href="https://github.com/ultralytics/yolov5/issues/2293">ultralytics/yolov5: label format</a> The annotated training and validation datasets contains insects of nine different species as listed below: <table> <tbody> <tr> <td>0 Coccinellidae septempunctata</td> </tr> <tr> <td>1 Apis mellifera</td> </tr> <tr> <td>2 Bombus lapidarius</td> </tr> <tr> <td>3 Bombus terrestris</td> </tr> <tr> <td>4 Eupeodes corolla</td> </tr> <tr> <td>5 Episyrphus balteatus</td> </tr> <tr> <td>6 Aglais urticae</td> </tr> <tr> <td>7 Vespula vulgaris</td> </tr> <tr> <td>8 Eristalis tenax</td> </tr> </tbody> </table> The test dataset contains additional classes of insects. <table> <tbody> <tr> <td>9 Non-Bombus Anthophila</td> </tr> <tr> <td>10 Bombus spp.</td> </tr> <tr> <td>11 Syrphidae</td> </tr> <tr> <td>12 Fly spp.</td> </tr> <tr> <td>13 Unclear insect</td> </tr> <tr> <td>14 Mixed animals: —————————— Rhopalocera Non-Anthophila Hymenoptera Non-Syrphidae Diptera Non-Conccinalidae Coleoptera Concinellidae Other animals</td> </tr> </tbody> </table> 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): <a href="https://github.com/ultralytics/yolov5">ultralytics/yolov5: YOLOv5  in PyTorch</a> 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

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 4
Harmonization: 8
Access: 16
Reuse readiness: 8
Engagement: 4