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AI Data Labeling

Data Labeling Solution for AI Training

"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"
            }
        }
    ]
}

"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"
    }
}

"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": []
            }
        }
    ]
}

"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
        }
    ]
}

"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
        }
    ]
}

"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"
        }
    }
]

"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
        }
    ]
}

"Analyze and categorizes emotions, intentions, etc. of natural language."

sentiment_and_intent_analysis
{
    "response": {
        "choice": "anger",
        "text_id": 81202745595
    }
}

"Collect topic and contextual natural language text from various users."

utterance_collection
{
    "response": {
        "q_id": 58239027713,
        "text": "환불 규정에 대해 자세히 알고 싶습니다."
    }
}

"Analyze the type and context of text and categorize it accurately"

text_classification
{
    "response": {
        "q_id": 34206743655,
        "choice": 3
    }
}

"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
    }
}

"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
    }
}

"Classify audio data to retrieve context and criteria."

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

"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
    }
}

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

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

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

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

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

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

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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