Browsing datasets can be quite troublesome, especially when the dataset is large.

npy (numpy array) and h5 files are two common data storage formats.
The drawback of h5 files is that they are prone to data corruption. I have encountered issues multiple times where h5 files could not be opened.
npy files have clear advantages in terms of read speed and file transfer. However, they are loaded entirely into memory at once, which can easily cause memory overflow if the server is not powerful enough.

Common image datasets typically separate labels and images, such as COCO. This allows you to use a file browser to view images and quickly observe their characteristics. However, in most cases, we don’t view images on a local computer but rather work with datasets on a server.

In 2024, when working with PyTorch, I find it more convenient to directly plot images using matplotlib. Matplotlib is generally used to display a single image, but using subplots allows you to display multiple images simultaneously. If OpenCV is used, you can overlay some label values onto the images. However, there is a drawback: if you are working on a remote server, transferring generated images can consume significant bandwidth.
Ultimately, the choice of method depends on your own judgment!