raoumer / srrescycgan

Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution

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Run raoumer/srrescycgan with an API

Use one of our client libraries to get started quickly. Clicking on a library will take you to the Playground tab where you can tweak different inputs, see the results, and copy the corresponding code to use in your own project.

Input schema

The fields you can use to run this model with an API. If you don't give a value for a field its default value will be used.

Field Type Default value Description
image
string
Input image to be upscaled
variant
string
jpeg-compression
Model variant (options: jpeg-compression, real-image-corruptions, sensor-noise, unknown-compressions)
no_chop
boolean
False
Don't chop the image (uses more memory)

Output schema

The shape of the response you’ll get when you run this model with an API.

Schema
{
  "type": "array",
  "items": {
    "type": "object",
    "properties": {
      "file": {
        "type": "string",
        "format": "uri",
        "x-order": 0
      },
      "text": {
        "type": "string",
        "x-order": 1
      }
    }
  },
  "x-cog-array-type": "iterator"
}