![]() ![]() during training) randomly crop the images to exactly the size you want. Notable Features: 800 image size increase, AI-driven automatic resizing, neural networks. The main goal is to make sure your images are fairly small so that they can be loaded from disk very quickly - ime data loading is frequently the bottleneck when training CNNs, and resizing your images to roughly the size you'll be training on is very helpful at reducing data loading times. Personally I opt for something between the two (I make it so the image's area is 65536 pixels). You may all have faced problems with distorted images at some point and hence would have tried to enhance the image quality. This is intentionally vague - I've seen people resize their images so the longest side is 256 pixels, I've seen other people resize them so the shortest side is 256 pixels. There is another closely related topic on adaptive image resizing that attempts to resize images/feature maps adaptively during training. Before training, resize your images so they're about the size you want to train on. The resizer module can handle arbitrary resolutions and aspect ratios which is very important for tasks like object detection and segmentation.You can do this before doing any training or during training ("on the fly") or some combination of both. So yes, images are cropped and scaled to be the same size. In theory convolutional neural networks can run on images of any size, but in practice libraries like Tensorflow and Pytorch assume all images in a batch have the same dimensions (for efficiency purposes).
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