Formats
Cogniac
Object Detection
Object DetectionIMPORTEXPORT
FEATURED
Object Detection

Cogniac

A JSON annotation format used by Cogniac.

Overview

The proprietary JSON annotation format used by the Cogniac computer vision platform.

Format Description

Below, learn the structure of Cogniac.


{
    "frame": 0,
    "filename": "./PATH_TO_FILE.ext",
    "object_name_1": [
        {
            "class_name_1": {
                "x0": 1334,
                "x1": 1657,
                "y0": 242,
                "y1": 553,
                "probability": 0.8974609375
            },
            "class_name_2": [],
            "class_name_3": []
        },
        {
            "class_name_1": {
                "x0": 7,
                "x1": 201,
                "y0": 31,
                "y1": 337,
                "probability": 0.87353515625
            },
            "class_name_2": [
                {
                    "text": "476240",
                    "x0": 137,
                    "x1": 156,
                    "y0": 128,
                    "y1": 206,
                    "probability": 0.755047082901001
                }
            ],
            "class_name_3": []
        },
        {
            "class_name_1": {
                "x0": 547,
                "x1": 844,
                "y0": 157,
                "y1": 442,
                "probability": 0.7021484375
            },
            "numbers": [],
            "initials": []
        }
    ],
    "object_name_2": [],
    "object_name_3": [],
    "object_name_4": [
        {
            "text": "36",
            "x0": 628,
            "x1": 728,
            "y0": 571,
            "y1": 641,
            "probability": 0.9057629108428955
        },
        {
            "text": "37",
            "x0": 987,
            "x1": 1101,
            "y0": 652,
            "y1": 730,
            "probability": 0.9051821231842041
        },
        {
            "text": "38",
            "x0": 1418,
            "x1": 1542,
            "y0": 757,
            "y1": 839,
            "probability": 0.9096717238426208
        }
    ]
}

Split and Merge Datasets

With Roboflow supervision, an open source Python package with utilities for completing computer vision tasks, you can merge and split detections in Cogniac. Read our dedicated guides to learn how to merge and split Cogniac detections.

Split detections in Cogniac

No items found.

Merge detections in Cogniac

No items found.

Supported Models

Below, see model architectures that require data in the Cogniac format when training a new model.

On each page below, you can find links to our guides that show how to plot predictions from the model, and complete other common tasks like detecting small objects with the model.

No items found.

On this page

Convert Data from Cogniac

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Convert Data to Cogniac

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Formats
Cogniac

What is Cogniac?

The proprietary JSON annotation format used by the Cogniac computer vision platform.

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Cogniac

Below, we show how to convert data to and from

Cogniac

. We also list popular models that use the

Cogniac

data format. Our conversion tools are free to use.

CONVERT To
CONVERT From
EXAMPLE

Convert Data to Cogniac

Use Roboflow to convert
Cogniac
to the following formats.

Roboflow is a trusted solution for converting and managing your data. Today, over 250,000 datasets are managed on Roboflow, comprised of 100 million labeled and annotated images.

With Roboflow, you get a solution with:
‍

Free data conversion

SOC II Type 2 Compliant

Trusted by 250,000+ developers

Cogniac
 to 
PaliGemma JSONL
Cogniac
 to 
YOLOv10 PyTorch TXT
Cogniac
 to 
YOLOv9 PyTorch TXT
Cogniac
 to 
Florence-2
Cogniac
 to 
YOLOv11 PyTorch TXT
Cogniac
 to 
OpenAI GPT-4o Object Detection JSONL
Cogniac
 to 
COCO Run-Length Encoding (RLE)
Cogniac
 to 
Segment Anything 2
Cogniac
 to 
YOLOv8 PyTorch TXT
Cogniac
 to 
YOLOv8 Oriented Bounding Boxes
Cogniac
 to 
Pascal VOC XML
Cogniac
 to 
Tensorflow TFRecord
Cogniac
 to 
COCO JSON
Cogniac
 to 
YOLO Darknet TXT
Cogniac
 to 
CreateML JSON
Cogniac
 to 
YOLOv7 PyTorch TXT
Cogniac
 to 
YOLOv5 PyTorch TXT
Cogniac
 to 
Google Cloud AutoML Vision CSV
Cogniac
 to 
Sagemaker GroundTruth Manifest
Cogniac
 to 
meituan/yolov6
Cogniac
 to 
Scaled-YOLOv4 TXT
Cogniac
 to 
OpenAI CLIP Classification
Cogniac
 to 
YOLO Keras TXT
Cogniac
 to 
RetinaNet Keras CSV
Cogniac
 to 
Tensorflow Object Detection CSV
Cogniac
 to 
Multiclass Classification CSV
Cogniac
 to 
YOLOv4 PyTorch TXT
Cogniac
 to 
YOLOv5 Oriented Bounding Boxes

Convert Data from Cogniac

Use Roboflow to convert the following formats to
Cogniac
format.

Roboflow is a trusted solution for converting and managing your data. Today, over 250,000 datasets are managed on Roboflow, comprised of 100 million labeled and annotated images.

With Roboflow, you get:
‍

Free data conversion

SOC II Type 1 Compliant

Trusted by 250,000+ developers

Roboflow is a trusted solution for converting and managing your data. Today, over 100,000 datasets are managed on Roboflow, comprised of 100 million labeled and annotated images.

Once your data is in Roboflow, just add the link from your dataset and you're ready to go. We even include the code to export to common inference formats like TFLite, ONNX, and CoreML.

Below are pre-configured models that use the
Cogniac
data format
.

What computer vision models use Cogniac?

The

No items found.

models all use the

Cogniac

data format.

We don't currently have models that use this annotation format.
To see our entire list of computer vision models, check out the Roboflow Model Library.

{
    "frame": 0,
    "filename": "./PATH_TO_FILE.ext",
    "object_name_1": [
        {
            "class_name_1": {
                "x0": 1334,
                "x1": 1657,
                "y0": 242,
                "y1": 553,
                "probability": 0.8974609375
            },
            "class_name_2": [],
            "class_name_3": []
        },
        {
            "class_name_1": {
                "x0": 7,
                "x1": 201,
                "y0": 31,
                "y1": 337,
                "probability": 0.87353515625
            },
            "class_name_2": [
                {
                    "text": "476240",
                    "x0": 137,
                    "x1": 156,
                    "y0": 128,
                    "y1": 206,
                    "probability": 0.755047082901001
                }
            ],
            "class_name_3": []
        },
        {
            "class_name_1": {
                "x0": 547,
                "x1": 844,
                "y0": 157,
                "y1": 442,
                "probability": 0.7021484375
            },
            "numbers": [],
            "initials": []
        }
    ],
    "object_name_2": [],
    "object_name_3": [],
    "object_name_4": [
        {
            "text": "36",
            "x0": 628,
            "x1": 728,
            "y0": 571,
            "y1": 641,
            "probability": 0.9057629108428955
        },
        {
            "text": "37",
            "x0": 987,
            "x1": 1101,
            "y0": 652,
            "y1": 730,
            "probability": 0.9051821231842041
        },
        {
            "text": "38",
            "x0": 1418,
            "x1": 1542,
            "y0": 757,
            "y1": 839,
            "probability": 0.9096717238426208
        }
    ]
}
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