Recognition Specification

Specification object

Definition

A recognition specification describes the output format of your recognition algorithm. You may attach multiple algorithms that perform the same task to the same specification in order to leverage automatic updates of your embedded recognition models.

Parameter	Type	Attributes	Description
id	int (string)	read-only	The ID of the recognition specification. This field is a string for public recognition models.
name	string		A short name for your recognition specification.
description	string		A longer description of your recognition specification.
update_date	string	read-only	Date time (ISO 8601 format) of the last update of the recognition specification.
metadata	object		A JSON field containing any kind of information that you may find interesting to store.
current_version_id	int	nullable, hidden for public models	The ID of the current recognition version object that this specification will execute if you ask it to perform an inference. This is convenient if you want to allow your app to point to a constant API endpoint while keeping the possibility to smoothly update the recognition model behind. This field is hidden for public recognition models
outputs	array(object)	hidden	The specification of the outputs you would like to recognize. It's an array of output objects. As this field tends to be large, it is hidden when you access the list of recognition models.

The id field is an integer for private models and a string for public recognition models.

Output object

Parameter	Type	Description
labels	object	An output of type labels.

Labels output

Parameter	Type	Description
labels	array(object)	A list of labels objects that will be recognized by your model.
exclusive	bool	A boolean describing if the declared labels are mutually exclusive or not.
roi	string	ROI stands for "Region Of Interest". Possible values are: NONE: if your model is performing classification only without object localisation BBOX: if your model can also output bounding boxes for the multiple objects detected in the image.

Label Object

Parameter	Type	Description
id	int	The numeric ID of your label. Can be anything you want, this ID will be present in the inference response for you to use it.
name	string	The name of the label. It will also be present in the inference response.

Create a specification

Definition

Creates a new recognition specification.

cURL

POST https://api.deepomatic.com/v0.7/recognition/specs

Python

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

client.RecognitionSpec.create(...)

Arguments

Parameter	Type	Default	Description
name	string		A short name for your recognition model.
description	string	""	A longer description of your recognition model.
metadata	object	{}	A JSON field containing any kind of information that you may find interesting to store.
outputs	object		An output object to describe how input data should be pre-processed.

Code sample

cURL

curl https://api.deepomatic.com/v0.7/recognition/specs \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}" \
-d '{
"name": "My recognition model",
"description": "To recognize various types of birds",
"metadata": {"author": "me", "project": "Birds 101"},
"outputs": [{"labels": {"roi": "NONE", "exclusive": true, "labels": [{"id": 0, "name": "hot-dog"}, {"id": 1, "name": "not hot-dog"}]}}]
}' \
-H "Content-Type: application/json"

Python

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

client.RecognitionSpec.create(
    name="hot-dog VS not hot-dog classifier",
    description="My great hot-dog VS not hot-dog classifier !",
    metadata={
        "author": "me",
        "project": "my secret project"
    },
    outputs = [{
        "labels": {
            "roi": "NONE",
            "exclusive": True,
            "labels": [{
                "id": 0,
                "name": "hot-dog"
            }, {
                "id": 1,
                "name": "not hot-dog"
            }]
        }
    }]
)

Response

A recognition specification object.

JSON

{
    "id": 42,
    "name": "hot-dog VS not hot-dog classifier",
    "description": "My great hot-dog VS not hot-dog classifier !",
    "task_id": 123,
    "update_date": "2018-02-16T16:37:25.148189Z",
    "metadata": {
        "author": "me",
        "project": "my secret project"
    },
    "outputs": [{
        "labels": {
            "roi": "NONE",
            "exclusive": true,
            "labels": [{
                "id": 0,
                "name": "hot-dog"
            }, {
                "id": 1,
                "name": "not hot-dog"
            }]
        }
    }],
    "current_version_id": null
}

List specifications

Definition

Get the list of existing recognition specifications.

cURL

# To list public specifications, use:
GET https://api.deepomatic.com/v0.7/recognition/public

# To list your own specifications, use:
GET https://api.deepomatic.com/v0.7/recognition/specs

Python

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

# To list public specifications, use:
client.RecognitionSpec.list(public=True)

# To list your own specifications, use:
client.RecognitionSpec.list()

Code sample

cURL

# For public specifications:
curl  https://api.deepomatic.com/v0.7/recognition/public \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}"

# For private networks:
curl https://api.deepomatic.com/v0.7/recognition/specs \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}"

Python

import os, deepomatic
client = deepomatic.Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

# For public specifications:
for spec in client.RecognitionSpec.list(public=True):
    print(spec)

# For private specifications:
for spec in client.RecognitionSpec.list():
    print(spec)

Response

A paginated list of responses.

Parameter	Type	Description
count	int	The total number of results.
next	string	The URL to the next page.
previous	string	The URL to the previous page.
results	array(object)	A list of your recognition specification objects. Please note that the outputfield is not present and that current_version_id is unavailable for public recognition models.

JSON

{
    "count": 2,
    "next": null,
    "previous": null,
    "results": [
        {
            "id": 42,
            "name": "My great hot-dog VS not hot-dog classifier !",
            "description": "Very complicated classifier",
            "update_date": "2018-03-09T18:30:43.404610Z",
            "current_version_id": 1,
            "metadata": {}
        },
        ...
    ]
}

Retrieve a specification

Definition

Retrieve a recognition specification by ID.

cURL

# To retrieve a public specification, use:
GET https://api.deepomatic.com/v0.7/recognition/public/{SPEC_ID}

# To retrieve your own specification, use:
GET https://api.deepomatic.com/v0.7/recognition/specs/{SPEC_ID}

Python

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

# {SPEC_ID} may be a string for a public specification
# or an integer for your own specification.
client.RecognitionSpec.retrieve({SPEC_ID})

Arguments

Parameter	Type	Default	Description
spec_id	int		The ID of the recognition specification to get.

Code sample

cURL

# For a public specification:
curl https://api.deepomatic.com/v0.7/recognition/public/fashion-v4 \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}"

# For a private specification:
curl https://api.deepomatic.com/v0.7/recognition/specs/42 \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}"

Python

import os, deepomatic
client = deepomatic.Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

# For a public specification:
client.RecognitionSpec.retrieve("fashion-v4")

# For a private specification:
client.RecognitionSpec.retrieve(42)

Response

A recognition specification object.

JSON

{
    "id": "fashion-v4",
    "name": "Fashion detector",
    "description": "",
    "update_date": "2018-03-08T19:24:26.528815Z",
    "metadata": {},
    "outputs": [
        {
            "labels": {
                "roi": "BBOX",
                "exclusive": true,
                "labels": [
                    {
                        "id": 0,
                        "name": "sweater"
                    },
                    {
                        "id": 1,
                        "name": "hat"
                    },
                    ...
                    {
                        "id": 14,
                        "name": "swimwear"
                    }
                ]
            }
        }
    ]
}

Edit a specification

Definition

Updates the specified specification by setting the values of the parameters passed. Any parameters not provided will be left unchanged.

This request accepts only the name, metadata and current_version_id arguments. Other values are immutable.

cURL

PATCH https://api.deepomatic.com/v0.7/recognition/specs/{SPEC_ID}

Python

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

spec = client.RecognitionSpec.retrieve({SPEC_ID})
spec.update(...)

Arguments

Parameter	Type	Attributes	Description
name	string	optional	A short name for your network.
description	string	optional	A longer description of your network.
metadata	object	optional	A JSON field containing any kind of information that you may find interesting to store.
current_version_id	int	optional	The ID of the current recognition version object that this specification will execute if you ask it to perform an inference. This is convenient if you want to allow your app to point to a constant API endpoint while keeping the possibility to smoothly update the recognition model behind.

Code sample

cURL

curl https://api.deepomatic.com/v0.7/recognition/specs/42 \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}" \
-d name='new name' \
-d current_version_id=123 \
-X PATCH

Python

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

spec = client.RecognitionSpec.retrieve(42)
spec.update(
    name="new name",
    current_version_id=123
)

Response

A recognition specification object.

Delete a specification

Definition

Permanently deletes a recognition specification. It cannot be undone. Attached resources like recognition versions will also be suppressed.

cURL

DELETE https://api.deepomatic.com/v0.7/recognition/specs/{SPEC_ID}

Python

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

spec = client.RecognitionSpec.retrieve({SPEC_ID})
spec.delete()

This cannot be undone. Attached resources like recognition versions will also be suppressed.

Arguments

Parameter	Type	Default	Description
spec_id	int		The ID of the specification to delete.

Code sample

cURL

curl https://api.deepomatic.com/v0.7/recognition/specs/42 \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}" \
-X DELETE

Python

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

spec = client.RecognitionSpec.retrieve(42)
spec.delete()

Response

Return 204 (no content).

Specification Inference

Definition

Run inference on the current version of the specification. Therefore, its current_version_id field must not be null. This endpoint returns a task ID.

cURL

# To run inference on a public specification, use:
POST https://api.deepomatic.com/v0.7/recognition/public/{SPEC_ID}/inference

# To run inference on your own specification, use:
POST https://api.deepomatic.com/v0.7/recognition/specs/{SPEC_ID}/inference

Python

import os
from deepomatic.api.client import Client
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

# {SPEC_ID} may be a string for a public specification
# or an integer for your own specification.
spec = client.RecognitionSpec.retrieve({SPEC_ID})
spec.inference(...)

Arguments

Parameter	Type	Default	Description
spec_id	int		The neural network ID
inputs	array(object)		The inputs of the neural network as an array of input objects. Must be non empty.
show_discarded	bool	false	A boolean indicating if the response must include labels which did not pass the recognition threshold.
max_predictions	int	100	The maximum number of predicted and discarded objects to return.

Input object

Parameter	Type	Description
image	object	An image input. It is the only supported type for now.

Image input

Parameter	Type	Default	Description
source	string		Either the URL of the image (in which case it must start with http: or https: ), or directly the binary content of the image (in which case it must start with data:image/jpeg;encoding, with encoding being either binaryor base64. jpeg might be replaced by another image type).
bbox	object	null	(optional) A bounding box object to crop the image before performing inference.

Code sample

cURL

URL=https://static.deepomatic.com/resources/demos/api-clients/dog2.jpg
# Inference from an URL:
curl https://api.deepomatic.com/v0.7/recognition/public/fashion-v4/inference \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}" \
-d "{\"inputs\":[{\"image\": {\"source\": \"${URL}\"}}]}" \
-d show_discarded="True" \
-H "Content-Type: application/json" \
-d max_predictions=100

# You can also directly send an image file or binary content using multipart/form-data:
curl ${URL} > /tmp/img.jpg
curl https://api.deepomatic.com/v0.7/recognition/public/fashion-v4/inference \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}" \
-F inputs[0]image.source=@/tmp/img.jpg

# You can also send base64 data by prefixing it with 'data:image/*;base64,' and sending it as application/json:
BASE64_DATA=$(cat /tmp/img.jpg | base64)
curl https://api.deepomatic.com/v0.7/recognition/public/fashion-v4/inference \
-H "X-API-KEY: ${DEEPOMATIC_API_KEY}" \
-H "Content-Type: application/json" \
-d "{\"inputs\":[{\"image\": {\"source\": \"data:image/*;base64,${BASE64_DATA}\"}}]}"

Python

import base64
import sys, tarfile
if sys.version_info >= (3, 0):
    from urllib.request import urlretrieve
else:
    from urllib import urlretrieve

import os
from deepomatic.api.client import Client
from deepomatic.api.inputs import ImageInput
client = Client(api_key=os.getenv('DEEPOMATIC_API_KEY'))

spec = client.RecognitionSpec.retrieve("fashion-v4")

# Inference from an URL:
url = "https://static.deepomatic.com/resources/demos/api-clients/dog2.jpg"
spec.inference(inputs=[ImageInput(url)], show_discarded=True, max_predictions=100)

# You can also directly send an image file:
urlretrieve(url, '/tmp/img.jpg')
with open('/tmp/img.jpg', 'rb') as fp:
    spec.inference(inputs=[ImageInput(fp)])

# You can also send binary data:
with open('/tmp/img.jpg', 'rb') as fp:
    binary_data = fp.read()

spec.inference(inputs=[ImageInput(binary_data, encoding="binary")])

# If you finally want to send base64 data, you can use:
base64_data = base64.b64encode(binary_data)
spec.inference(inputs=[ImageInput(base64_data, encoding="base64")])

Response

On success, this endpoint will return a task ID. See Inference output section for a description of the response format.

JSON Response

{
    "task_id": "123"
}

JSON Task Data

{
    "outputs": [{
        "labels": {
            "predicted": [{
                "label_id": 9,
                "label_name": "sunglasses",
                "score": 0.990304172,
                "threshold": 0.347,
                "roi": {
                    "region_id": 1,
                    "bbox": {
                        "xmin": 0.312604159,
                        "ymin": 0.366485775,
                        "ymax": 0.5318923,
                        "xmax": 0.666821837
                    }
                }
            }],
            "discarded": []
        }
    }]
}