LOCBEEF: Beef Quality Image dataset for Deep Learning Models

The LOCBEEF dataset contains 3268 images of local Aceh beef collected from 07:00 a.m - 22:00 p.m, more information about the clock is shown in Fig. The dataset contains two categories of directories, namely train, and test. Furthermore, each subdirectory consists of fresh and rotten. An example of the image can be seen in Figs. 2 and 3. The directory structure for the data is shown in Fig. 1. The image directory for train contains 2228 images each subdirectory contains 1114 images, and the test directory contains 980 images for each subdirectory containing 490 images. For images have a resolution of 176 x 144 pixel, 320 x 240 pixel, 640 x 480 pixel, 720 x 480 pixel, 720 x 720 pixel, 1280 x 720 pixel, 1920 x 1080 pixel, 2560 x 1920 pixel, 3120 x 3120 pixel, 3264 x 2248 pixel, and 4160 x 3120 pixel.

The classification of LOCBEEF datasets has been carried out using the deep learning method of Convolutional Neural Networks with an image composition of 70% training data and 30% test data. Images with the mentioned dimensions are included in the LOCBEEF dataset to apply to the Resnet50.

LOCBEEF数据集包含3268张亚齐本地牛肉图像，采集时段为每日7:00至22:00，更多关于采集时间的细节详见附图。该数据集设有训练（train）与测试（test）两类目录，每个子目录下均包含“新鲜”与“腐坏”两个子分类。图像示例详见图2与图3，数据集的目录结构如图1所示。训练集目录共包含2228张图像，每个子分类各有1114张；测试集目录共包含980张图像，每个子分类各有490张。本数据集涵盖的图像分辨率包括176×144像素、320×240像素、640×480像素、720×480像素、720×720像素、1280×720像素、1920×1080像素、2560×1920像素、3120×3120像素、3264×2248像素及4160×3120像素。

LOCBEEF数据集的分类任务采用卷积神经网络（Convolutional Neural Networks）这一深度学习方法实现，训练数据与测试数据的划分比例为7:3。为适配ResNet50模型，本数据集纳入了上述多种分辨率的图像。