ShrimpView: A Versatile Dataset for Shrimp Detection and Recognition

The "ShrimpView: A Versatile Dataset for Shrimp Detection and Recognition" is a meticulously curated collection of 10,000 samples (each with 11 attributes) designed to facilitate the training of deep learning models for shrimp detection and classification. Each sample in this dataset is associated with an image and accompanied by 11 categorical attributes. These attributes span a range of features including species type ("Pacific White Shrimp," "Tiger Prawn," "Ghost Shrimp"), life stage ("Larvae," "Juvenile," "Adult"), and environmental conditions ("Freshwater," "Saltwater," "Brackish Water"), among others. Additionally, the dataset incorporates variations in image orientation, background, and lighting conditions to enhance model generalizability. With resolutions ranging from 640x480 px to 1920x1080 px, the dataset is well-suited for both object detection and multi-class classification tasks. The variability in these attributes aims to improve the model's generalizability and robustness. For instance, the "Species" and "Life Stage" attributes can aid in multi-class classification, while "Color" and "Size" add complexity for object detection. "Orientation" and "Background" introduce viewpoint and environmental variance, respectively. "Lighting" conditions can simulate different capture scenarios, and "Resolution" offers scale variance. "Grouping" prepares the model for detecting multiple instances in a single frame, and "Habitat" ensures the model is trained for different water conditions. It aims to serve as a robust foundation for developing comprehensive machine learning solutions in the domain of aquatic species recognition. This rich, multi-dimensional dataset is thus ideal for training a robust deep learning model for comprehensive shrimp detection and classification. The computational costs involved in generating the dataset are listed as follows:Data Augmentation Cost: Caug=20 secondsCSV Generation Cost: Ccsv=1.1 secondsAttribute Randomization Cost: Cattr=1.1 secondsTotal Disk Space Needed: Dtotal=550 MBTotal Time Needed: CTotal=22.2 secondsTotal Time Needed with Parallelization: Ctotal, Cparallel=55.5 seconds (4 cores)《ShrimpView：一款用于虾类检测与识别的通用数据集》是一套经过细致甄选的10000条样本集（每条样本包含11项属性），旨在为虾类检测与分类的深度学习模型提供训练支撑。该数据集的每条样本均关联一张图像，并附带11项分类属性。这些属性涵盖多个维度，包括物种类型（"凡纳滨对虾""斑节对虾""鬼虾"）、生长阶段（"幼体""稚虾""成体"）以及环境条件（"淡水""海水""咸淡水"）等。 此外，为提升模型的泛化能力，数据集还纳入了图像朝向、背景与光照条件等多种变化因素。该数据集的图像分辨率覆盖640×480像素至1920×1080像素区间，可同时适配目标检测与多分类两类任务。 这些属性的多样性旨在提升模型的泛化性与鲁棒性。例如，"物种"与"生长阶段"属性可辅助多分类任务，而"颜色"与"尺寸"则为目标检测任务增添了复杂度；"朝向"与"背景"分别引入了视角与环境差异，"光照"条件可模拟不同的采集场景，"分辨率"则提供了尺度变化因素；"分组"属性可让模型学会检测单帧画面中的多个目标，"栖息环境"属性则确保模型能够针对不同水环境条件完成训练。 本数据集旨在为水生生物识别领域的全面机器学习解决方案提供坚实基础。这款丰富的多维度数据集，是训练用于虾类全面检测与分类的鲁棒深度学习模型的理想选择。 生成该数据集所需的计算成本如下： • 数据增强成本：Caug=20秒 • CSV文件生成成本：Ccsv=1.1秒 • 属性随机化成本：Cattr=1.1秒 • 所需总磁盘空间：Dtotal=550 MB • 总耗时：CTotal=22.2秒 • 并行化处理总耗时：Ctotal, Cparallel=55.5秒（4核心）

搜集汇总

背景与挑战

背景概述

该数据集是一个包含10,000个样本的虾类检测和识别数据集，每个样本包含一张图片和11个分类属性，适用于深度学习模型的训练。数据集涵盖了多种虾类、生命阶段和环境条件，旨在提高模型的泛化能力和鲁棒性。