Add transfer learning block

Add transfer learning block

Adds a transfer learning block.

POSThttps://studio.edgeimpulse.com/v1/api/organizations/{organizationId}/transfer-learning

Path parameters

organizationId*integer

Organization ID

Body

application/json

name*string

dockerContainer*string

description*string

operatesOn*OrganizationTransferLearningOperatesOn (enum)

object_detectionaudioimageregressionother

objectDetectionLastLayerObjectDetectionLastLayer (enum)

mobilenet-ssdfomoyolov2-akidayolov5yolov5v5-drpaiyoloxyolov7tao-retinanettao-ssdtao-yolov3tao-yolov4

implementationVersion*integer

isPublicboolean

Whether this block is publicly available to Edge Impulse users (if false, then only for members of the owning organization)

isPublicForDevicesarray of string

If isPublic is true, the list of devices (from latencyDevices) for which this model can be shown.

publicProjectTierAvailabilityPublicProjectTierAvailability (enum)

For public blocks, this indicates the project tiers for which this block is available.

enterprise-onlypro-or-enterpriseall-projects

repositoryUrlstring

URL to the source code of this custom learn block.

parametersarray of object

List of parameters, spec'ed according to https://docs.edgeimpulse.com/docs/tips-and-tricks/adding-parameters-to-custom-blocks

imageInputScalingImageInputScaling (enum)

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.

0..1-1..1-128..1270..255torchbgr-subtract-imagenet-mean

indRequiresGpuboolean

If set, requires this block to be scheduled on GPU.

Response

200

OK

Body

application/json

success*boolean

Whether the operation succeeded

errorstring

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

id*integer

Request

JavaScriptCurl

const response = await fetch('https://studio.edgeimpulse.com/v1/api/organizations/{organizationId}/transfer-learning', {
    method: 'POST',
    headers: {
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      "name": "text",
      "dockerContainer": "text",
      "description": "text",
      "operatesOn": "object_detection"
    }),
});
const data = await response.json();

Response

{
  "success": false,
  "error": "text"
}