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 Figure. The dataset contains two categories of directories, namely train, and test. Furthermore, each subdirectory consists of fresh and rotten. 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 meat image is placed on a plate with a beef size of ± 10x10 cm, then taken using a smartphone that has an open camera application installed to get a different resolution. The smartphone uses a tripod with a distance to the meat of 20 cm at room temperature between 28-30 °Celsius and lighting using lamps. 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 ResNet-50.