The phrase "amounts of image" is not correct in standard written English.
It is unclear and does not convey a specific meaning, as "amounts" typically refers to quantities of countable or measurable items, while "image" is generally uncountable in this context.
Example: "The amounts of image data collected for the project were overwhelming."
Alternatives: "quantities of images" or "volumes of image data".
Virtual observatories give us access to huge amounts of image data that are often redundant.
The context: Machines need to be trained on massive amounts of image data in order to recognize objects.
However, the massive amounts of image data generated via high‐throughput screening require enormous manual processing time and effort, restricting the practical use of such an assay.
The Sloan Digital Sky Survey has used digital sensors to scan distant galaxies from an optical telescope in New Mexico, collecting vast amounts of image data that are processed with powerful computers.
Whereas this information can significantly improve understanding of disease pathophysiology as well as the noninvasive diagnosis of disease in patients, the need to process, analyze, and store large amounts of image data presents a great challenge.
Shooting movies at higher frame rates can be complicated because it generates huge amounts of image data, which is best stored on high-capacity solid-state drives and necessitates additional computing power when rendering visual effects with computer-generated imagery (CGI).
In life sciences, there is a growing need for new informatics tools designed to provide automated solutions in order to analyze big amounts of images obtained from high-throughput imaging systems.
There is a rich literature on medical image segmentation, but many approaches especially deep learning require large amounts of images and operate on information from only a single given imaging modality.
Even during single surveys by ROVs or towed systems large amounts of images are compiled.
"We collect large amounts of images," says Sophie Lebrecht, the chief executive at Neon Labs.
Today we can throw massive amounts of images into a deep learning algorithm and fairly accurately pick out a cow from the field in which it's grazing.
