I generally use the so-called benchmark/state-of-the-art image recognition datasets: MNIST, CIFAR10, ImageNet, COOC, Cityscapes, and KITTI. They are open-source datasets, and more databases with free access can be found on popular web pages like Papers With Code and Kaggle.For a self-collected dataset that needs to be stored independently, I generally use cloud storage or storage provided by the university. But for projects where we need to train ML models on Amazon Web Services (AWS), we store a dataset there. For some projects, I use Google Collab Pro. Some of my students also use NVIDIA DIGITS.The choice of the data set depends on the task. Since my research is focused on image classification and the improvement of algorithms, I use CIFAR10 and ImageNet more often. I normally work with 3-4 datasets simultaneously to compare the performance of my algorithms.