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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 most popular use cases of image to image is the style transfer. Style transfer models can convert a regular photography into a painting in the style of a famous painter. | ||
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| ## Task Variants | ||
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| ### Image inpainting | ||
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| Image inpainting is widely used during photography editing to remove unwanted objects, such as poles, wires or sensor | ||
| 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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| ## 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 = { | ||
| datasets: [ | ||
| { | ||
| description: "Synthetic dataset, for image relighting", | ||
| id: "VIDIT" | ||
| }, | ||
| { | ||
| description: "Multiple images of celebrities, used for facial expression translation" | ||
| id: "CelebA" | ||
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| } | ||
| ], | ||
| demo: { | ||
| inputs: [ | ||
| { | ||
| filename: "image-to-image-input.jpeg", | ||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Is this repo still open for contribution? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Very cool! |
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| type: "img", | ||
| }, | ||
| ], | ||
| outputs: [ | ||
| { | ||
| filename: "image-to-image-output.png", | ||
| type: "img", | ||
| }, | ||
| ] | ||
| }, | ||
| 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, contrast and structure of two images. The values of SSIM range between -1 and 1, and higher values indicate closer resemblance to the original image.", | ||
| 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: "A model that enhances images captured in low light conditions.", | ||
| id: "keras-io/low-light-image-enhancement" | ||
| }, | ||
| { | ||
| description: "A model that increases the resolution of an image.", | ||
| id: "keras-io/super-resolution" | ||
| }, | ||
| { | ||
| description: "A model that creates a set of variations of the imput image in the style of DALL-E using Stable Diffusion.", | ||
| id: "lambdalabs/sd-image-variations-diffusers" | ||
| } | ||
| ], | ||
| summary: "Image to image is the task of transforming a source image to match the characteristics of a target image or a target image domain. Any image manipulation and enhancement is possible with image to image models." | ||
| widgetModels: [""], | ||
| youtubeId: "", | ||
| }; | ||
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| export default taskData; | ||

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