DeepWeedsX 在澳大利亚北部收集的大型杂草物种图像数据集

DeepWeedsX 数据集由 9 个类别的 17,508 个独特的 256x256 彩色图像组成。有 15,007 张训练图像和 2,501 张测试图像。这些图像是从澳大利亚北部的八个牧场环境中原位收集的。 与澳大利亚北部的土地护理团体和业主联络，最终选择了八种目标杂草 用于收集大型杂草物种图像数据集； Chinee Apple (Ziziphus mauritiana)、Lantana、Parkinsonia (Parkinsonia aculeata)、Parthenium (Parthenium hysterophorus)、Prickly Acacia (Vachellianilotica)、橡胶藤 (Cryptostegia grandiflora)、暹罗杂草 (Chromolaena odorata) 和蛇草 (Stachytarphetaspp)。 DeepWeedsX 是 DeepWeeds 数据集的一个子集，最初由 Alex Olsen 收集，之前已公开访问。我们提出了一个带有明确定义的训练和测试数据集的标记变体。可以使用标记的训练数据集的子集构建验证数据集以进行参数优化。 All class label files consist of Comma Seperated Values (CSVs) detailing the label and species, for example: 20161207-111327-0.jpg, 0 denotes that 20161207-111327-0.jpg belongs to class 0 (Chinee Apple). Class and species labels are as follows: 0- Chinee Apple 1- Lantana 2- Parkinsonia 3- Parthenium 4- Prickly Acacia 5- Rubber Vine 6- Siam Weed 7- Snake Weed 8- Other. All images are compressed in a single ZIP archive, and are labelled as per the class file labels. To cite the DeepWeedsX dataset, kindly use the following BibTex entry: @ARTICLE{8693488, author={C. {Lammie} and A. {Olsen} and T. {Carrick} and M. R. {Azghadi}}, journal={IEEE Access}, title={Low-Power and High-Speed Deep FPGA Inference Engines for Weed Classification at the Edge}, year={2019}, volume={}, number={}, pages={1-1}, keywords={Machine Learning (ML);Deep Neural Networks (DNNs);Convolutional Neural Networks (CNNs);Binarized Neural Networks (BNNs);Internet of Things (IoT);Field Programmable Gate Arrays (FPGAs);High-level Synthesis (HLS);Weed Classification}, doi={10.1109/ACCESS.2019.2911709}, ISSN={2169-3536}, month={},} All original data collection was funded by the Australian Government Department of Agriculture and Water Resources Control Tools and Technologies for Established Pest Animals and Weeds Programme (Grant No. 4-53KULEI).