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Image to image #370
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| ## Use Cases | ||
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| ### Style transfer | ||
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| One of the first and most popular applications, style transfer can convert a regular photography into a paining in the | ||
| style of Claude Monet. | ||
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| ### Image segementation | ||
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| Image segmentation represents is a pixel level classification - each pixel is assigned a numeric label, according to | ||
| the object it belongs to. | ||
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| ### Image inpainting | ||
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| Image inpaining is widely used during photography editing to remove unwanted objects, such as poles, wires or sensor | ||
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| dust. | ||
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| ### Image colorization | ||
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| Old, black and white images can be brought up to life using an image colorization model. | ||
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| ### Super Resolution | ||
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| Super resolution models increase the resolution of an image, allowing for higher quality viewing and printing. | ||
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| ## Task Variants | ||
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| This place can be filled with variants of this task if there's any. | ||
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| ## Inference | ||
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| This section should have useful information about how to pull a model from Hugging Face Hub that is a part of a library specialized in a task and use it. | ||
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| ## Useful Resources | ||
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| In this area, you can insert useful resources about how to train or use a model for this task. | ||
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| import { type TaskDataCustom } from "../Types"; | ||
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| const taskData: TaskDataCustom = { | ||
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| datasets: [ | ||
| { | ||
| description: "Synthetic dataset, simulating fog on real scenes", | ||
| id: "FoggyCityscapes" | ||
| }, | ||
| { | ||
| description: "Synthetic dataset, for image relighting", | ||
| id: "VIDIT" | ||
| }, | ||
| { | ||
| description: "A collection of selfie images and similar faces from anime" | ||
| id: "selfie2anime" | ||
| }, | ||
| { | ||
| description: "Multiple images of celebrities, used for facial expression translation" | ||
| id: "CelebA" | ||
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| } | ||
| ], | ||
| demo: { | ||
| inputs: [], | ||
| outputs: [], | ||
| }, | ||
| isPlaceholder: false, | ||
| metrics: [ | ||
| { | ||
| description: "Peak Signal to Noise Ratio (PSNR) is an approximation of the human perception, considering the ratio of the absolute intensity with respect to the variations. Measured in dB, a high value indicates a high fidelity.", | ||
| id: "PSNR" | ||
| }, | ||
| { | ||
| description: "Structural Similarity Index (SSIM) is a perceptual metric which compares the luminance, constrast and structure of two images. Between -1 and 1, the higer values indicate closer resemblance.", | ||
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| id: "SSIM" | ||
| }, | ||
| { | ||
| description: "Inception Score (IS) is an analysis of the labels predicted by an image classification model when presented with a sample of the generated images.", | ||
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| id: "IS" | ||
| }, | ||
| ], | ||
| models: [ | ||
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| { | ||
| description: "Enhances images captures in low light conditions", | ||
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| id: "keras-io/low-light-image-enhancement" | ||
| } | ||
| { | ||
| description: "Increases the resolution of an image", | ||
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| id: "keras-io/super-resolution" | ||
| } | ||
| { | ||
| description: "Transforms photographies into a Japanese animation style image", | ||
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| id: "akiyamasho/AnimeBackgroundGAN-Shinkai" | ||
| } | ||
| ], | ||
| summary: "Image to image models aim at transforming a source image to match the characteristics of a target image or a target image domain. There is an endless list of image manipulations and enhancements possible using image to image machine learning models." | ||
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| widgetModels: [], | ||
| youtubeId: "", | ||
| }; | ||
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| export default taskData; | ||
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