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AI Data Labeling

Data Labeling Solution for AI Training

Video Annotation
Semantic Segmentation
Bounding Box
Polygon
Polyline
Keypoint

"Label image data frame by frame and provide it in many forms."

video_annotation 
{
    "frame": 31197,
    "video": {
        "fps": 29.98,
        "url": "https://datamaker.io/api/example/video_annotation/GJt9eqjT32yg4.mp4"
    },
    "annotations": [
        {
            "label": {
                "data": {
                    "x": 54,
                    "y": 624,
                    "width": 22,
                    "height": 55
                },
                "category": "rect"
            },
            "classification": {
                "code": "pedestrian"
            }
        },
        {
            "label": {
                "data": {
                    "x": 237,
                    "y": 618,
                    "width": 31,
                    "height": 83
                },
                "category": "rect"
            },
            "classification": {
                "code": "pedestrian"
            }
        },
        {
            "label": {
                "data": {
                    "x": 269,
                    "y": 582,
                    "width": 620,
                    "height": 246
                },
                "category": "rect"
            },
            "classification": {
                "code": "vehicle"
            }
        },
        {
            "label": {
                "data": {
                    "x": 1493,
                    "y": 157,
                    "width": 128,
                    "height": 66
                },
                "category": "rect"
            },
            "classification": {
                "code": "traffic_light"
            }
        },
        {
            "label": {
                "data": {
                    "x": 1,
                    "y": 992,
                    "width": 1917,
                    "height": 86
                },
                "category": "rect"
            },
            "classification": {
                "code": "vehicle"
            }
        },
        {
            "label": {
                "data": {
                    "x": 1210,
                    "y": 673,
                    "width": 214,
                    "height": 88
                },
                "category": "rect"
            },
            "classification": {
                "code": "vehicle"
            }
        },
        {
            "label": {
                "data": {
                    "x": 1340,
                    "y": 675,
                    "width": 291,
                    "height": 105
                },
                "category": "rect"
            },
            "classification": {
                "code": "vehicle"
            }
        },
        {
            "label": {
                "data": {
                    "x": 1563,
                    "y": 686,
                    "width": 111,
                    "height": 78
                },
                "category": "rect"
            },
            "classification": {
                "code": "vehicle"
            }
        },
        {
            "label": {
                "data": {
                    "x": 1679,
                    "y": 707,
                    "width": 26,
                    "height": 45
                },
                "category": "rect"
            },
            "classification": {
                "code": "vehicle"
            }
        },
        {
            "label": {
                "data": {
                    "x": 1696,
                    "y": 667,
                    "width": 223,
                    "height": 140
                },
                "category": "rect"
            },
            "classification": {
                "code": "vehicle"
            }
        }
    ]
}
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"Semantic segmentation gives classes to all pixels of image, image data."

semantic_segmentation 
{
    "response": {
        "images": {
            "labeled_all": {
                "image": "https://datamaker.io/api/example/321988_full.png"
            },
            "labeled_layers": {
                "Tree": "https://datamaker.io/api/example/tree.png",
                "Asphalt": "https://datamaker.io/api/example/asphalt.png",
                "Building": "https://datamaker.io/api/example/building.png",
                "Obstacle": "https://datamaker.io/api/example/obstacle.png",
                "Lane Marks": "https://datamaker.io/api/example/lane_marks.png",
                "Pedestrian": "https://datamaker.io/api/example/pedestrian.png",
                "Sky or Void": "https://datamaker.io/api/example/sky_or_void.png"
            }
        },
        "classes": {
            "Tree": {
                "color": "#00ff00"
            },
            "Asphalt": {
                "color": "#808080"
            },
            "Building": {
                "color": "#ffa500"
            },
            "Obstacle": {
                "color": "#ff00ff"
            },
            "Lane Marks": {
                "color": "#00ffff"
            },
            "Pedestrian": {
                "color": "#ffff00"
            },
            "Sky or Void": {
                "color": "#ffffff"
            }
        },
        "user_task__id": "321988"
    }
}
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"Image, label objects in image data with bounding box."

bounding_box 
{
    "annotations": [
        {
            "label": {
                "data": {
                    "x": 1,
                    "y": 371,
                    "width": 232,
                    "height": 786
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 276,
                    "y": 368,
                    "width": 421,
                    "height": 794
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 708,
                    "y": 343,
                    "width": 454,
                    "height": 819
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 1211,
                    "y": 352,
                    "width": 422,
                    "height": 807
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 1726,
                    "y": 352,
                    "width": 402,
                    "height": 916
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 2215,
                    "y": 343,
                    "width": 422,
                    "height": 831
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 2700,
                    "y": 360,
                    "width": 444,
                    "height": 756
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 3176,
                    "y": 351,
                    "width": 416,
                    "height": 654
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 3653,
                    "y": 349,
                    "width": 349,
                    "height": 755
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 3658,
                    "y": 1784,
                    "width": 344,
                    "height": 808
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 3183,
                    "y": 1788,
                    "width": 473,
                    "height": 811
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 2724,
                    "y": 1747,
                    "width": 403,
                    "height": 839
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 2247,
                    "y": 1788,
                    "width": 397,
                    "height": 791
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 1768,
                    "y": 1753,
                    "width": 392,
                    "height": 836
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 1256,
                    "y": 1795,
                    "width": 418,
                    "height": 798
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 803,
                    "y": 1740,
                    "width": 402,
                    "height": 834
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 277,
                    "y": 1775,
                    "width": 470,
                    "height": 804
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        },
        {
            "label": {
                "data": {
                    "x": 1,
                    "y": 1782,
                    "width": 293,
                    "height": 791
                },
                "category": "bounding_box"
            },
            "classification": {
                "code": "house",
                "attributes": []
            }
        }
    ]
}
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"Delicate and accurate labeling of irregular boundaries of objects in image, image data."

polygon 
{
    "vertices": [
        {
            "x": 10,
            "y": 402
        },
        {
            "x": 21,
            "y": 399
        },
        {
            "x": 30,
            "y": 394
        },
        {
            "x": 43,
            "y": 385
        },
        {
            "x": 50,
            "y": 360
        },
        {
            "x": 66,
            "y": 380
        },
        {
            "x": 72,
            "y": 401
        },
        {
            "x": 79,
            "y": 420
        },
        {
            "x": 91,
            "y": 449
        },
        {
            "x": 120,
            "y": 470
        },
        {
            "x": 134,
            "y": 482
        },
        {
            "x": 151,
            "y": 502
        },
        {
            "x": 162,
            "y": 563
        },
        {
            "x": 175,
            "y": 523
        },
        {
            "x": 195,
            "y": 507
        },
        {
            "x": 211,
            "y": 492
        },
        {
            "x": 250,
            "y": 475
        },
        {
            "x": 290,
            "y": 447
        }
    ]
}
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"Label linear image data, such as lanes, accurately up to pixels."

polyline 
{
    "vertices": [
        {
            "x": 1,
            "y": 586
        },
        {
            "x": 232,
            "y": 589
        },
        {
            "x": 1,
            "y": 491
        },
        {
            "x": 201,
            "y": 496
        },
        {
            "x": 1,
            "y": 432
        },
        {
            "x": 175,
            "y": 470
        },
        {
            "x": 1,
            "y": 394
        },
        {
            "x": 149,
            "y": 427
        }
    ]
}
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"Specify key feature points for objects to track and recognize objects."

keypoint 
[
    {
        "label": {
            "data": {
                "x": 51,
                "y": 324
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 287,
                "y": 272
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 464,
                "y": 80
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 128,
                "y": 45
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 388,
                "y": 199
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 184,
                "y": 109
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 40,
                "y": 229
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 337,
                "y": 133
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 333,
                "y": 189
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 171,
                "y": 24
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 93,
                "y": 235
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 403,
                "y": 20
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 393,
                "y": 31
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 328,
                "y": 182
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 360,
                "y": 142
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 389,
                "y": 124
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 101,
                "y": 329
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 51,
                "y": 324
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 287,
                "y": 272
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 464,
                "y": 80
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 128,
                "y": 45
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 388,
                "y": 199
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 184,
                "y": 109
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 40,
                "y": 229
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 337,
                "y": 133
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 333,
                "y": 189
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 171,
                "y": 24
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 93,
                "y": 235
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 403,
                "y": 20
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 393,
                "y": 31
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 328,
                "y": 182
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 360,
                "y": 142
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 389,
                "y": 124
            },
            "category": "keypoint"
        }
    },
    {
        "label": {
            "data": {
                "x": 101,
                "y": 329
            },
            "category": "keypoint"
        }
    }
]
See more
Named Entity Recognition
Sentiment & Intent Analysis
Utterance Collection
Text Classification
OCR Transcription

"Identify types with Named Entity Recognition by natural language comprehension. "

named_entity_recognition 
{
    "annotations": [
        {
            "end": 10,
            "label": "Transportation",
            "start": 8,
            "string": "지하철",
            "text_id": 1854380577
        },
        {
            "end": 23,
            "label": "Transportation",
            "start": 23,
            "string": "차",
            "text_id": 1854380577
        }
    ]
}
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"Analyze and categorizes emotions, intentions, etc. of natural language."

sentiment_and_intent_analysis 
{
    "response": {
        "choice": "anger",
        "text_id": 81202745595
    }
}
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"Collect topic and contextual natural language text from various users."

utterance_collection 
{
    "response": {
        "q_id": 58239027713,
        "text": "환불 규정에 대해 자세히 알고 싶습니다."
    }
}
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"Analyze the type and context of text and categorize it accurately"

text_classification 
{
    "response": {
        "q_id": 34206743655,
        "choice": 3
    }
}
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"Reads text in images accurately and efficiently."

ocr_transcription 
{
    "transcription": {
        "items": [
            {
                "name": "갤럭시 Z플립",
                "order": 1,
                "price": 1650000
            },
            {
                "name": "갤럭시 버즈 +",
                "order": 2,
                "price": 179300
            }
        ],
        "store": "삼성전자 주식회사",
        "total": 1829300
    }
}
See more
Audio Transcription
Audio Categorization
Audio Segmentation

"Transcribe voice data into text for speech recognition engine development."

audio_transcription 
{
    "response": {
        "text": "이런 좋은 에너지와 경기력을 계속 이어간다면 충분히 좋은 시즌을 보내지 않을까 생각한다",
        "task_id": 5823932913
    }
}
See more

"Classify audio data to retrieve context and criteria."

audio_categorization 
{
    "response": {
        "choice": 2,
        "task_id": 5823932937
    }
}
See more

"Decrease or adjust audio length to classify sound sources or improve artificial intelligence learning rates."

audio_segmentation 
{
    "response": {
        "slicing": [
            {
                "file": "https://datamaker.io/api/example/audio_slicing/jujihoon_1.wav",
                "category": "jujihoon"
            },
            {
                "file": "https://datamaker.io/api/example/audio_slicing/jujihoon_2.wav",
                "category": "jujihoon"
            },
            {
                "file": "https://datamaker.io/api/example/audio_slicing/jujihoon_3.wav",
                "category": "jujihoon"
            },
            {
                "file": "https://datamaker.io/api/example/audio_slicing/baedoona_1.wav",
                "category": "baedoona"
            },
            {
                "file": "https://datamaker.io/api/example/audio_slicing/baedoona_2.wav",
                "category": "baedoona"
            },
            {
                "file": "https://datamaker.io/api/example/audio_slicing/baedoona_3.wav",
                "category": "baedoona"
            }
        ],
        "task_id": 5823932985
    }
}
See more

How it works

how-it-works
step-1
Raw Datasets

We are provided with raw datasets
and a work guideline. Raw datasets
can be via API, .CSV, FTP, Cloud services, JPG, etc.

step-1
Project Setup

We prepare our project and workers
according to the guideline. This is a
pilot phase to get our workers ready.

step-1
Data Labeling

With the help of AI preprocessing,
our workers label the data.

step-1
Quality Control

All annotations are double-checked by our
inspectors with strict standards to
produce high accuracy datasets.

step-1
Product Delivery

We deliver our labeled data to our clients via API, etc.

benefit
Data Labeling

Big Data Solution

Data Crawling
Data Preparation
Database
Data Visualization
Software Development

"Extract web data and deliver classified datasets in the form of APIs, CSVs, etc."

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"Perfect data processing and analysis utilization by consistently refining random data."

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"Build a database that can manage data securely and efficiently."

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"Visually represent data so that information can be effectively viewed at a glance."

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"Customize the services and capabilities required for referral projects to deliver custom deliverables."

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feature-1
Best Quality

Garbage in, Garbage out!
With our AI inspection engine and
inspection system, we offer datasets with high accuracy.

feature-2
Highest Production

동일한 라벨링 예산이라면,
저희 데이터메이커가 가장 많은 양의
데이터를 가공해드립니다.

feature-3
Strong Security

데이터메이커 작업자들은
통제된 데이터 랩에서 제공된 디바이스로만
데이터에 접근할 수 있습니다.

Clients

The Ultimate Data Labeling Platform

Inquire to Datamaker👨🏻‍💻

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