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fabric defect detection

Object Detection

Dataset Versions

Try Pre-Trained Model

2024-07-10 2:08pm

Generated on Jul 10, 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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46 Total Images

View All Images

Dataset Split

Train Set 91%

42Images

Valid Set %

0Images

Test Set 9%

4Images

Preprocessing

Resize: Stretch to 128x128

Augmentations

Outputs per training example: 3

Flip: Horizontal, Vertical

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

Grayscale: Apply to 15% of images

Similar Projects

Annotations

Group:

fabric-defects

360_F_131431319_kVXntZiX4m7gDkL7k3XsRjASeY0AUSg5.jpg

128x128

0.02MP

Updated Apr 17, 56494

3:37AM

GMT+00:00

Generated by Roboflow

Training Set

Transforms

Resize

Stretch to 128x128

Flip

Vertical

90° Rotate

Applied

Grayscale

Applied

Raw Data

Source Data

                            {
    "camera": "Generated by Roboflow",
    "datasets": [
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    "height": 128,
    "id": "EguT2L2MPXJYc58eJiBx",
    "label": [],
    "labels": [],
    "name": "360_F_131431319_kVXntZiX4m7gDkL7k3XsRjASeY0AUSg5.jpg",
    "numSteps": 4,
    "owner": "aDUgNLwl9ddQDQwwIRYuPuXWYzE2",
    "preprocessing": [
        "resize:[\"Stretch to\",128,128]",
        "flip:[\"vertical\"]",
        "ninety:[0]",
        "grayscale"
    ],
    "preprocessingParsed": [
        {
            "name": "Resize",
            "value": "Stretch to 128x128"
        },
        {
            "name": "Flip",
            "value": "Vertical"
        },
        {
            "name": "90° Rotate",
            "value": "Applied"
        },
        {
            "name": "Grayscale",
            "value": "Applied"
        }
    ],
    "source": "EguT2L2MPXJYc58eJiBx",
    "split": "train",
    "split.XO1yB0z7X3oZLbwKS1CX.3": "train",
    "status": "generated",
    "transforms": "[\n    \"resize:[\\\"Stretch to\\\",128,128]\",\n    \"flip:[\\\"vertical\\\"]\",\n    \"ninety:[0]\",\n    \"grayscale\"\n]",
    "updated": 1720620502649,
    "updatedDate": "Apr 17, 56494",
    "updatedTime": "3:37AM",
    "updatedTimezone": "+00:00",
    "versions": [
        "XO1yB0z7X3oZLbwKS1CX/3"
    ],
    "width": 128
}

Annotation Data

{
    "boxes": [
        {
            "type": "polygon",
            "label": "defect",
            "x": 60,
            "y": 65.1708335,
            "width": 20,
            "height": 84.29166699999999,
            "points": [
                [
                    51,
                    34.075
                ],
                [
                    50,
                    39.741667
                ],
                [
                    50.5,
                    44.841667
                ],
                [
                    53.25,
                    50.225
                ],
                [
                    52,
                    54.191667
                ],
                [
                    54.25,
                    59.008333
                ],
                [
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                [
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                    64.675
                ],
                [
                    54.75,
                    67.791667
                ],
                [
                    53.75,
                    69.208333
                ],
                [
                    53.75,
                    75.158333
                ],
                [
                    56,
                    77.425
                ],
                [
                    56.5,
                    83.375
                ],
                [
                    58,
                    86.491667
                ],
                [
                    56.75,
                    88.191667
                ],
                [
                    58.5,
                    93.575
                ],
                [
                    56.75,
                    96.125
                ],
                [
                    57,
                    104.341667
                ],
                [
                    59.125,
                    107.316667
                ],
                [
                    63.625,
                    107.316667
                ],
                [
                    65.5,
                    106.041667
                ],
                [
                    67,
                    93.858333
                ],
                [
                    68.5,
                    92.158333
                ],
                [
                    68,
                    87.625
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                [
                    64.75,
                    84.508333
                ],
                [
                    67.25,
                    82.241667
                ],
                [
                    67.5,
                    78.841667
                ],
                [
                    70,
                    73.741667
                ],
                [
                    67,
                    70.625
                ],
                [
                    66,
                    65.525
                ],
                [
                    67,
                    63.825
                ],
                [
                    66.5,
                    60.991667
                ],
                [
                    68.75,
                    58.725
                ],
                [
                    69.5,
                    51.358333
                ],
                [
                    67.75,
                    47.108333
                ],
                [
                    67.25,
                    40.875
                ],
                [
                    61.75,
                    31.525
                ],
                [
                    61.75,
                    29.258333
                ],
                [
                    56.5,
                    23.025
                ],
                [
                    55,
                    23.875
                ],
                [
                    53.75,
                    30.958333
                ],
                [
                    51,
                    34.075
                ]
            ]
        },
        {
            "type": "polygon",
            "label": "defect",
            "x": 83.375,
            "y": 30.1791665,
            "width": 8.25,
            "height": 17.708333,
            "points": [
                [
                    82.5,
                    26.708333
                ],
                [
                    79.25,
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                ],
                [
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                    38.325
                ],
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                    80.375,
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                ],
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                    36.483333
                ],
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                ],
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                    29.541667
                ],
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                    28.691667
                ],
                [
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                    24.158333
                ],
                [
                    82.75,
                    21.325
                ],
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                    23.308333
                ],
                [
                    79.75,
                    26.708333
                ],
                [
                    82.5,
                    26.708333
                ]
            ]
        },
        {
            "type": "polygon",
            "label": "defect",
            "x": 16.875,
            "y": 22.5291665,
            "width": 13.25,
            "height": 13.741666999999998,
            "points": [
                [
                    12.25,
                    23.875
                ],
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                    12,
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                ],
                [
                    14.125,
                    29.4
                ],
                [
                    18.375,
                    26
                ],
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                    23.5,
                    24.725
                ],
                [
                    21.625,
                    21.466667
                ],
                [
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                ],
                [
                    10.25,
                    17.641667
                ],
                [
                    10.25,
                    20.758333
                ],
                [
                    12.25,
                    23.875
                ]
            ]
        }
    ],
    "height": 128,
    "key": "360_F_131431319_kVXntZiX4m7gDkL7k3XsRjASeY0AUSg5.jpg",
    "width": 128
}

fabric defect detection

Dataset Versions

2024-07-10 2:08pm

Popular Download Formats

46 Total Images

Dataset Split

Preprocessing

Augmentations

Similar Projects

Annotations

Unused Classes

Tags

Attributes

360_F_131431319_kVXntZiX4m7gDkL7k3XsRjASeY0AUSg5.jpg

Updated Apr 17, 56494

Generated by Roboflow

Training Set

Transforms

Annotation History

Raw Data

Source Data

Annotation Data