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Mimar Floor Plan Detection Model

Object Detection

Dataset Versions

Try Pre-Trained Model

datatset mixed segments and boxes 2025-01-16 3-34pm

2024-09-30 4:32pm

Generated on Sep 30, 2024

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Popular Download Formats

YOLOv11

TXT annotations and YAML config used with YOLOv11.

YOLOv9

TXT annotations and YAML config used with YOLOv9.

YOLOv8

TXT annotations and YAML config used with YOLOv8.

YOLOv5

TXT annotations and YAML config used with YOLOv5.

YOLOv7

TXT annotations and YAML config used with YOLOv7.

COCO JSON

COCO JSON annotations are used with EfficientDet Pytorch and Detectron 2.

YOLO Darknet

Darknet TXT annotations used with YOLO Darknet (both v3 and v4) and YOLOv3 PyTorch.

Pascal VOC XML

Common XML annotation format for local data munging (pioneered by ImageNet).

TFRecord

TFRecord binary format used for both Tensorflow 1.5 and Tensorflow 2.0 Object Detection models.

PaliGemma

PaliGemma JSONL format used for fine-tuning PaliGemma, Google's open multimodal vision model.

CreateML JSON

CreateML JSON format is used with Apple's CreateML and Turi Create tools.

Other Formats

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1052 Total Images

View All Images

Dataset Split

Train Set 87%

912Images

Valid Set 8%

79Images

Test Set 5%

52Images

Preprocessing

Auto-Orient: Applied

Resize: Fit (white edges) in 640x640

Grayscale: Applied

Augmentations

Outputs per training example: 3

Flip: Horizontal, Vertical

90° Rotate: Clockwise, Counter-Clockwise, Upside Down

Crop: 0% Minimum Zoom, 20% Maximum Zoom

Rotation: Between -15° and +15°

Similar Projects

Annotations

Group: doors

bed2

chair

door

door2

sofa1

sofa2

sofa3

table

toilet

vanity

Unused Classes

bathtub

bed

shower

sink

stove

Attributes

707bf47e71db606bf93f83a97a36edc7.jpg

640x640

0.41MP

Updated Jan 31, 56719

7:44AM

GMT+00:00

Generated by Roboflow

Training Set

Transforms

Auto-Orient

Applied

Crop

Keep 98%
Centered on 68%, 10%

Resize

Fit (white edges) in 640x640

Grayscale

Applied

Flip

Vertical

90° Rotate

Applied

Rotation

-9°

Raw Data

Source Data

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    "camera": "Generated by Roboflow",
    "datasets": [
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    "destination": "d5c269be6864518288f54ed4a6158077",
    "height": 640,
    "id": "DW2NNSgkYDKMMpADEli6",
    "label": [],
    "labels": [],
    "name": "707bf47e71db606bf93f83a97a36edc7.jpg",
    "numSteps": 7,
    "owner": "oVJrKZRo0FlpjUjBgUuD",
    "preprocessing": [
        "auto-orient",
        "crop:[98,0.676716,0.102793]",
        "resize:[\"Fit (white edges) in\",640,640]",
        "grayscale",
        "flip:[\"vertical\"]",
        "ninety:[0]",
        "rotate:[-9]"
    ],
    "preprocessingParsed": [
        {
            "name": "Auto-Orient",
            "value": "Applied"
        },
        {
            "name": "Crop",
            "value": "Keep 98%<br />Centered on 68%, 10%"
        },
        {
            "name": "Resize",
            "value": "Fit (white edges) in 640x640"
        },
        {
            "name": "Grayscale",
            "value": "Applied"
        },
        {
            "name": "Flip",
            "value": "Vertical"
        },
        {
            "name": "90° Rotate",
            "value": "Applied"
        },
        {
            "name": "Rotation",
            "value": "-9°"
        }
    ],
    "source": "DW2NNSgkYDKMMpADEli6",
    "split": "train",
    "split.ZNYI0cEIn51ssmkq9v10.3": "train",
    "status": "generated",
    "transforms": "[\n    \"auto-orient\",\n    \"crop:[98,0.676716,0.102793]\",\n    \"resize:[\\\"Fit (white edges) in\\\",640,640]\",\n    \"grayscale\",\n    \"flip:[\\\"vertical\\\"]\",\n    \"ninety:[0]\",\n    \"rotate:[-9]\"\n]",
    "updated": 1727714130269,
    "updatedDate": "Jan 31, 56719",
    "updatedTime": "7:44AM",
    "updatedTimezone": "+00:00",
    "versions": [
        "ZNYI0cEIn51ssmkq9v10/3"
    ],
    "width": 640
}

Annotation Data

{
    "boxes": [
        {
            "label": "door",
            "x": 190.5,
            "y": 409,
            "width": 49,
            "height": 48
        },
        {
            "label": "door",
            "x": 200.5,
            "y": 352.5,
            "width": 48,
            "height": 50
        },
        {
            "label": "door",
            "x": 213,
            "y": 273.5,
            "width": 47,
            "height": 44
        },
        {
            "label": "door",
            "x": 316.5,
            "y": 302.5,
            "width": 49,
            "height": 50
        },
        {
            "label": "door",
            "x": 232.5,
            "y": 463,
            "width": 48,
            "height": 49
        },
        {
            "label": "door",
            "x": 519,
            "y": 489,
            "width": 38,
            "height": 36
        },
        {
            "label": "door2",
            "x": 388.5,
            "y": 565.5,
            "width": 48,
            "height": 36
        },
        {
            "label": "door2",
            "x": 403.5,
            "y": 145,
            "width": 51,
            "height": 39
        },
        {
            "label": "bed2",
            "x": 281.5,
            "y": 556,
            "width": 114,
            "height": 106
        },
        {
            "label": "bed2",
            "x": 102,
            "y": 467.5,
            "width": 115,
            "height": 104
        },
        {
            "label": "bed2",
            "x": 149.5,
            "y": 86.5,
            "width": 115,
            "height": 108
        },
        {
            "label": "toilet",
            "x": 114.5,
            "y": 334,
            "width": 35,
            "height": 31
        },
        {
            "label": "toilet",
            "x": 126,
            "y": 261,
            "width": 33,
            "height": 26
        },
        {
            "label": "vanity",
            "x": 262,
            "y": 327,
            "width": 25,
            "height": 26
        },
        {
            "label": "sofa3",
            "x": 574,
            "y": 263,
            "width": 62,
            "height": 106
        },
        {
            "label": "sofa2",
            "x": 485.5,
            "y": 171,
            "width": 76,
            "height": 51
        },
        {
            "label": "sofa1",
            "x": 520,
            "y": 322.5,
            "width": 49,
            "height": 39
        },
        {
            "label": "sofa1",
            "x": 541,
            "y": 190,
            "width": 46,
            "height": 37
        },
        {
            "label": "table",
            "x": 479,
            "y": 404,
            "width": 83,
            "height": 53
        },
        {
            "label": "chair",
            "x": 493,
            "y": 435.5,
            "width": 24,
            "height": 23
        },
        {
            "label": "chair",
            "x": 526,
            "y": 411.5,
            "width": 23,
            "height": 25
        },
        {
            "label": "chair",
            "x": 503,
            "y": 379.5,
            "width": 23,
            "height": 22
        },
        {
            "label": "chair",
            "x": 464.5,
            "y": 373.5,
            "width": 23,
            "height": 22
        },
        {
            "label": "chair",
            "x": 432.5,
            "y": 396.5,
            "width": 22,
            "height": 23
        },
        {
            "label": "chair",
            "x": 455.5,
            "y": 428.5,
            "width": 24,
            "height": 23
        },
        {
            "label": "chair",
            "x": 530.5,
            "y": 256,
            "width": 41,
            "height": 65
        }
    ],
    "height": 640,
    "key": "707bf47e71db606bf93f83a97a36edc7.jpg",
    "width": 640
}

Mimar Floor Plan Detection Model

Dataset Versions

2024-09-30 4:32pm

Popular Download Formats

1052 Total Images

Dataset Split

Preprocessing

Augmentations

Similar Projects

Annotations

Unused Classes

Attributes

707bf47e71db606bf93f83a97a36edc7.jpg

Updated Jan 31, 56719

Generated by Roboflow

Training Set

Transforms

Annotation History

Raw Data

Source Data

Annotation Data