The dataset CottonWeedDet12 consists of 5648 RGB images of 12-class weeds that are common in cotton fields in the southern U.S. states, with a total of 9370 bounding boxes. These images were acquired by either smartphones or hand-held digital cameras, under natural field light condition and throughout June to September of 2021. The images were manually labeled by qualified personnel for weed identification, and the labeling process was done using the VGG Image Annotator (version 2.10). The dataset, at the time of publication, is the largest publicly available multi-class dataset dedicated to weed detection. It expects to facilitate communicate efforts to exploit state-of-the-art deep learning method to push weed recognition to the next level. With the WeedDet12 dataset, a performance benchmark of a suite of YOLO object detectors has been built for weed detection. Detailed documentation of the dataset, model benchmarking and performance results is given in an accompanying journal paper paper: Dang, F., Chen, D., Lu, Y., Li, Z., 2023. YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2023.107655 If you use the dataset on a published publication, please cite the dataset or the journal article above.

{"references": ["Dang, F., Chen, D., Lu, Y., Li, Z., 2023.\u00a0YOLOWeeds: A novel benchmark of YOLO object detectors for\u00a0multi-class weed detection in cotton production systems. Computers and Electronics in Agriculture.\u00a0https://doi.org/10.1016/j.compag.2023.107655"]}