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Cryptomatte images can be created by several 3D graphic programs like Blender, [2] Autodesk Maya, [3] Autodesk 3ds Max [4] or Houdini [5] and are usually exported using the OpenEXR file format. Whether a program is able to generate a cryptomatte or not is determined by the render engine being used.
500 natural images, explicitly separated into disjoint train, validation and test subsets + benchmarking code. Based on BSDS300. Each image segmented by five different subjects on average. 500 Segmented images Contour detection and hierarchical image segmentation 2011 [11] University of California, Berkeley: Microsoft Common Objects in Context ...
The code is hosted on GitHub, and community support forums include the GitHub issues page, and a Slack channel. [citation needed] In addition to standard neural networks, Keras has support for convolutional and recurrent neural networks. It supports other common utility layers like dropout, batch normalization, and pooling. [12]
Examples of algorithms for this task include New Edge-Directed Interpolation (NEDI), [1] [2] Edge-Guided Image Interpolation (EGGI), [3] Iterative Curvature-Based Interpolation (ICBI), [citation needed] and Directional Cubic Convolution Interpolation (DCCI). [4] A study found that DCCI had the best scores in PSNR and SSIM on a series of test ...
A reference implementation rewritten in Python 3.6 with the PyTorch 0.4.0 library was released by the author under the Apache 2.0 license: deep-image-prior [3] A TensorFlow -based implementation written in Python 2 and released under the CC-SA 3.0 license: deep-image-prior-tensorflow
An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more.This is accomplished by doing a convolution between the kernel and an image.
For the CLIP image models, the input images are preprocessed by first dividing each of the R, G, B values of an image by the maximum possible value, so that these values fall between 0 and 1, then subtracting by [0.48145466, 0.4578275, 0.40821073], and dividing by [0.26862954, 0.26130258, 0.27577711].