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：用于虾类检测与识别的通用数据集》是一套精心构建的数据集，共包含10000条样本，每条样本均附带11项属性，旨在助力虾类检测与分类相关深度学习模型的训练工作。每条样本均对应一张图像，并附带11项分类属性。这些属性涵盖了物种类型（凡纳滨对虾（Pacific White Shrimp）、斑节对虾（Tiger Prawn）、鬼虾（Ghost Shrimp））、生长阶段（幼体（Larvae）、稚虾（Juvenile）、成体（Adult））以及环境条件（淡水（Freshwater）、海水（Saltwater）、咸淡水（Brackish Water））等多个维度。此外，为提升模型的泛化能力，本数据集还涵盖了图像朝向（Orientation）、背景（Background）与光照（Lighting）条件的多种变化。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。 上述属性的多样性设计，旨在提升模型的泛化能力与鲁棒性。例如，「物种（Species）」与「生长阶段（Life Stage）」属性可辅助多分类任务，「体色（Color）」与「体型（Size）」则为目标检测任务增加了识别复杂度；「朝向（Orientation）」与「背景（Background）」分别引入了视角与环境层面的差异；「光照（Lighting）」条件可模拟不同的采集场景，「分辨率（Resolution）」则提供了尺度变化维度；「集群状态（Grouping）」可让模型学会在单帧图像中检测多个虾类实例，「生境（Habitat）」则确保模型可适配不同的水环境条件。本数据集旨在为水生生物识别领域的综合性机器学习解决方案开发提供坚实的基础，这款丰富的多维度数据集因此非常适合用于训练鲁棒性优异的虾类综合检测与分类深度学习模型。 本数据集生成过程中的计算成本如下： • 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（4核心）：Ctotal, Cparallel=55.5秒

搜集汇总

背景与挑战

背景概述

ShrimpView是一个包含10,000个样本的数据集，每个样本配有图像和11个分类属性，涵盖物种、生命阶段和环境条件等特征，适用于虾类检测和识别的深度学习模型训练。数据集通过图像方向、背景和光照条件的变化增强泛化能力，分辨率范围从640x480到1920x1080，支持目标检测和多类分类任务，并提供了计算成本信息以辅助使用。