raoumer
/
srrescycgan
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution
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)
|
{
"type": "object",
"properties": {
"image": {
"type": "string",
"format": "uri",
"x-order": 0,
"description": "Input image to be upscaled"
},
"no_chop": {
"type": "boolean",
"default": false,
"x-order": 2,
"description": "Don't chop the image (uses more memory)"
},
"variant": {
"type": "string",
"default": "jpeg-compression",
"x-order": 1,
"description": "Model variant (options: jpeg-compression, real-image-corruptions, sensor-noise, unknown-compressions)"
}
}
}
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"
}