Epigos offers an API that allows you to make predictions from your model. This API is accessible within the Epigos Node SDK.

You’ll need to Create an API Key to Authentication with Epigos AI.

Image classification

You can predict the class or label of an image using the predict() function when you load a classification model.

classification()

Loads classification model deployed on the platform.

Param	Type	Description
`model_id`	string	The unique ID of your model deployed on Epigos AI.

import { Epigos } from "@epigosai/epigos-sdk";

const client = new Epigos({ apiKey: "api_key_123" });

// load classification model
const model = client.classification("modelId");

predict()

Makes predictions for the classification model.

Param	Type	Description
`payload`	dict	Dictionary containing prediction parameters

Payload

Param	Type	Description
`imageUrl`	string	Optional remote url pointing to image the
`imageBase64`	string	Optional Base64-encoded string of the image
`confidence`	number	Optional confidence threshold used to filter out predictions.

The function expects either imageUrl or imageBase64 to provided else it will raise validation errors.

Example prediction

// make predictions
results = await model.predict({ imageUrl: 'path/to/your/image.jpg' })

console.log(results)
{
   "category" : "cat",
   "confidence" : 0.854,
   "predictions" : [
      {
         "category" : "cat",
         "confidence" : 0.854
      },
      {
         "category" : "dog",
         "confidence" : 0.146
      }
   ]
}

Object detection

You can detect objects in an image using the detect() function when you load an object detection model.

objectDetection()

Loads an object detection model deployed on the platform.

Param	Type	Description
`model_id`	string	The unique ID of your model deployed on Epigos AI.

import { Epigos } from "@epigosai/epigos-sdk";

const client = new Epigos({ apiKey: "api_key_123" });

// load object detection model
const model = client.objectDetection("modelId");

detect()

Makes detection for the object detection model.

Param	Type	Description
`payload`	dict	Dictionary containing prediction parameters

Payload

Param	Type	Description
`imageUrl`	string	Optional remote url pointing to image the
`imageBase64`	string	Optional Base64-encoded string of the image
`confidence`	number	Optional confidence threshold used to filter out predictions.
`options`	object	Optional options to customize image annotation.

Options

Param	Type	Description
`annotate`	boolean	Optional annotate flag to specify whether to annotate the image or not.
`showProb`	boolean	Optional show_prob to specify whether detection confidence are shown on the labels.
`strokeWidth`	number	Optional stroke width to specify bounding box border width.

The function expects either imageUrl or imageBase64 to provided else it will raise validation errors.

Example detection

// make detections
results = await model.detect({ imageUrl: 'path/to/your/image.jpg' })

console.log(results)
{
   "detections": [
        {
            "label": "car",
            "confidence": 0.954,
            "x": 281,
            "y": 187,
            "width": 46,
            "height": 36
        }
    ],
    "image": "<base64 image>",
    "labelsCount": [
        {
            "label": "car",
            "total": 1
        }
    ]
}

Example detection with annotation options

// make detections
results = await model.detect({ imageUrl: 'path/to/your/image.jpg', options: {annotate: true, strokeWidth: 3} })

console.log(results)
{
   "detections": [
        {
            "label": "car",
            "confidence": 0.954,
            "x": 281,
            "y": 187,
            "width": 46,
            "height": 36
        }
    ],
    "image": "<base64 image>",
    "labelsCount": [
        {
            "label": "car",
            "total": 1
        }
    ]
}

Checkout the code on Github

​Image classification

​classification()

​predict()

​Payload

​Example prediction

​Object detection

​objectDetection()

​detect()

​Payload

​Options

​Example detection

​Example detection with annotation options

Image classification

classification()

predict()

Payload

Example prediction

Object detection

objectDetection()

detect()

Payload

Options

Example detection

Example detection with annotation options