Last updated: Aug-18-2025
OverviewManually cropping individual images with graphic editing tools to maintain the focal point can be time-intensive. Cloudinary offers its machine-learning and AI-powered content-aware cropping as a solution to this problem.
Let's introduce you to the different gravity options, including hard-coded directional types, and a variety of automatic detection options.
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What is gravity-specific cropping?   0:07 By applying the gravity transformation parameter for an image URL, you can instruct Cloudinary to automatically detect which part of the original image to retain, no matter what cropping, scaling or padding options are applied. Compass-direction cropping   1:08 Compass-style directions represent a location in the image. For example,g_north_east represents the top right corner, whereas g_south_west designates the bottom-left corner. Content-aware cropping - facial detection   3:08 One way to create a more reusable cropping style is to have it locate interesting or important aspects in the image. For example, we may want to automatically crop on the most prominent face in a group. This can be done using g_face, thanks to Cloudinary's facial recognition capabilities. Automatically crop to the most interesting area (g_auto) Use g_auto:subject on images   4:24 By training our neural networks with large datasets, we have created a way to determine the area of the image to which the human eye will be drawn. You can take advantage of this through the g_auto:subject transformation, providing better crop results with very challenging content, such as images with multiple faces. Crop to specific objects in an image   5:45 Rather than playing with compass-style directions or using our automatic cropping transformations, you can let Cloudinary detect the object with g_auto:<object>, for example g_auto:cake, using our Cloudinary AI Content Analysis add-on. This transformation helps to ensure the object is kept when the image is cropped. Keep learning If you like this, you might also like...
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