diff --git a/tasks/assets/image-to-image/image-to-image-input.jpeg b/tasks/assets/image-to-image/image-to-image-input.jpeg new file mode 100644 index 000000000..b59d26c8d Binary files /dev/null and b/tasks/assets/image-to-image/image-to-image-input.jpeg differ diff --git a/tasks/assets/image-to-image/image-to-image-output.png b/tasks/assets/image-to-image/image-to-image-output.png new file mode 100644 index 000000000..aaa37f451 Binary files /dev/null and b/tasks/assets/image-to-image/image-to-image-output.png differ diff --git a/tasks/src/image-to-image/about.md b/tasks/src/image-to-image/about.md new file mode 100644 index 000000000..289d4dd82 --- /dev/null +++ b/tasks/src/image-to-image/about.md @@ -0,0 +1,28 @@ +## Use Cases + +### Style transfer + +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. + +## Task Variants + +### Image inpainting + +Image inpainting is widely used during photography editing to remove unwanted objects, such as poles, wires or sensor +dust. + +### Image colorization + +Old, black and white images can be brought up to life using an image colorization model. + +### Super Resolution + +Super resolution models increase the resolution of an image, allowing for higher quality viewing and printing. + +## Inference + +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. + +## Useful Resources + +In this area, you can insert useful resources about how to train or use a model for this task. diff --git a/tasks/src/image-to-image/data.ts b/tasks/src/image-to-image/data.ts new file mode 100644 index 000000000..33d5f8918 --- /dev/null +++ b/tasks/src/image-to-image/data.ts @@ -0,0 +1,62 @@ +import { type TaskDataCustom } from "../Types"; + +const taskData: TaskDataCustom = { + datasets: [ + { + description: "Synthetic dataset, for image relighting", + id: "VIDIT" + }, + { + description: "Multiple images of celebrities, used for facial expression translation" + id: "huggan/CelebA-faces" + } + ], + demo: { + inputs: [ + { + filename: "image-to-image-input.jpeg", + 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.", + id: "IS" + }, + ], + models: [ + { + 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: "", +}; + +export default taskData;