Test pretrained model using image data

curl --request POST \
  --url https://studio.edgeimpulse.com/v1/api/{projectId}/pretrained-model/test-image \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '
{
  "imageFileBase64": "<string>",
  "input": {
    "resizeMode": "squash",
    "inputScaling": "0..1"
  },
  "model": {
    "modelType": "classification",
    "labels": [
      "<string>"
    ]
  }
}
'

{
  "success": true,
  "error": "<string>",
  "result": {},
  "boundingBoxes": [
    {
      "label": "<string>",
      "x": 123,
      "y": 123,
      "width": 123,
      "height": 123,
      "score": 123
    }
  ]
}

POST

api

{projectId}

pretrained-model

test-image

Test pretrained model using image data

curl --request POST \
  --url https://studio.edgeimpulse.com/v1/api/{projectId}/pretrained-model/test-image \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '
{
  "imageFileBase64": "<string>",
  "input": {
    "resizeMode": "squash",
    "inputScaling": "0..1"
  },
  "model": {
    "modelType": "classification",
    "labels": [
      "<string>"
    ]
  }
}
'

{
  "success": true,
  "error": "<string>",
  "result": {},
  "boundingBoxes": [
    {
      "label": "<string>",
      "x": 123,
      "y": 123,
      "width": 123,
      "height": 123,
      "score": 123
    }
  ]
}

Authorizations

x-api-key

string

header

required

Path Parameters

projectId

integer

required

Project ID

Query Parameters

impulseId

integer

Impulse ID. If this is unset then the default impulse is used.

Body

application/json

imageFileBase64

string

required

A base64 encoded input image file

input

object

required

Show child attributes

input.resizeMode

enum<string>

required

Input images are resized automatically before training and testing, to match the impulse input shape. This determines the resize mode used when the aspect ratio of the input data is different to the aspect ratio of the impulse.

Available options:

squash,

fit-short,

fit-long,

crop

Example:

"squash"

input.inputScaling

enum<string>

required

Normalization that is applied to images. If this is not set then 0..1 is used. "0..1" gives you non-normalized pixels between 0 and 1. "-1..1" gives you non-normalized pixels between -1 and 1. "0..255" gives you non-normalized pixels between 0 and 255. "-128..127" gives you non-normalized pixels between -128 and 127. "torch" first scales pixels between 0 and 1, then applies normalization using the ImageNet dataset (same as torchvision.transforms.Normalize()). "bgr-subtract-imagenet-mean" scales to 0..255, reorders pixels to BGR, and subtracts the ImageNet mean from each channel.

Available options:

0..1,

-1..1,

-128..127,

0..255,

torch,

bgr-subtract-imagenet-mean

model

object

required

Option 1
Option 2
Option 3

Show child attributes

model.modelType

enum<string>

required

Available options:

classification

model.labels

string[]

required

Response

200 - application/json

success

boolean

required

Whether the operation succeeded

error

string

Optional error description (set if 'success' was false)

result

object

Classification value per label. For a neural network this will be the confidence, for anomalies the anomaly score.

Show child attributes

result.{key}

number

boundingBoxes

object[]

Show child attributes

boundingBoxes.label

string

required

boundingBoxes.x

number

required

boundingBoxes.y

number

required

boundingBoxes.width

number

required

boundingBoxes.height

number

required

boundingBoxes.score

number

required

Test pretrained model

Profile pretrained model

⌘I

OVERVIEW

STUDIO API ENDPOINTS

Test pretrained model using image data

Authorizations

Path Parameters

Query Parameters

Body

Response