The two terms, "image resolution" and "image dimensions", are often used interchangeably in colloquial speech, but they absolutely have distinct technical definitions, especially in the context of digital imaging and machine learning.
Image Dimensions refers to the number of pixels along the width and height of an image. When we say an image is 1980x1080, these are the image's dimensions. It means the image is 1980 pixels wide and 1080 pixels high.
In a digital context, resolution can refer to the density of pixels in an image. It's often measured in PPI (Pixels Per Inch) or DPI (Dots Per Inch). Higher PPI/DPI values mean more detail can be represented in the same physical space. However, in many digital contexts, "resolution" is also used to refer to the total number of pixels in an image (i.e., the dimensions), which can be confusing.
In your contect (ML and CV), when we resize images, we are generally changing the image dimensions, reducing the total number of pixels in the image. The concept of PPI/DPI is less relevant here because we're typically not concerned with how the image will be physically displayed or printed.
A major source of this confusion comes from laptop/monitor marketing. 1920×1080 is the FHD (Full High Definition) resolution and that was/is the branding that a lot of laptops and monitors were sold with. Similarly, for video playback we say so and so resolution but it is not entirely correct since the bitrate can drastically change the quality of a certain resolution.
To avoid confusion, when writing about your work, it's best to use "image dimensions" when you're talking about the number of pixels along the width and height of the image, and "resolution" when you're discussing the level of detail in the image, which could be affected by both the dimension and the pixel density (PPI/DPI in physical display contexts). If you're using "resolution" to refer to dimensions (as is often done), just be sure to clarify what you mean.