Dataset Versions

v10

2023-07-17 11:45am

Generated on Jul 17, 2023

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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.

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4369 Total Images

View All Images

Dataset Split

Train Set 88%

3825Images

Valid Set 8%

357Images

Test Set 4%

187Images

Preprocessing

Auto-Orient: Applied

Resize: Fit (reflect edges) in 640x640

Augmentations

Outputs per training example: 3

Hue: Between -80° and +80°

Saturation: Between -50% and +50%

Brightness: Between -35% and +35%

Mosaic: Applied

Similar Projects

Annotations

Group:

Coverall

000028.jpg

640x640

0.41MP

Updated Jun 27, 55510

10:03PM

GMT+00:00

Generated by Roboflow

Training Set

Transforms

Auto-Orient

Applied

Resize

Fit (reflect edges) in 640x640

Raw Data

Source Data

                            {
    "camera": "Generated by Roboflow",
    "datasets": [
        "MAInG4fOxCUaLNGXmusT"
    ],
    "destination": "39479e303f7a8e9bc6e76c1a06fcbcf8",
    "height": 640,
    "id": "orTUX8UCDvWgDw5jwz4g",
    "label": [],
    "labels": [],
    "name": "000028.jpg",
    "numSteps": 2,
    "owner": "k8GRDjawnxdtgDI5yzLOl8oDrc03",
    "preprocessing": [
        "auto-orient",
        "resize:[\"Fit (reflect edges) in\",640,640]"
    ],
    "preprocessingParsed": [
        {
            "name": "Auto-Orient",
            "value": "Applied"
        },
        {
            "name": "Resize",
            "value": "Fit (reflect edges) in 640x640"
        }
    ],
    "source": "orTUX8UCDvWgDw5jwz4g",
    "split": "train",
    "split.MAInG4fOxCUaLNGXmusT.10": "train",
    "split.MAInG4fOxCUaLNGXmusT.8": "train",
    "split.MAInG4fOxCUaLNGXmusT.9": "train",
    "status": "generated",
    "transforms": "[\n    \"auto-orient\",\n    \"resize:[\\\"Fit (reflect edges) in\\\",640,640]\"\n]",
    "updated": 1689574543432,
    "updatedDate": "Jun 27, 55510",
    "updatedTime": "10:03PM",
    "updatedTimezone": "+00:00",
    "versions": [
        "MAInG4fOxCUaLNGXmusT/10",
        "MAInG4fOxCUaLNGXmusT/8",
        "MAInG4fOxCUaLNGXmusT/9"
    ],
    "width": 640
}

Annotation Data

{
    "boxes": [
        {
            "label": "Without_Coverall",
            "x": 426,
            "y": 331,
            "width": 120,
            "height": 310
        },
        {
            "label": "Without_Coverall",
            "x": 246,
            "y": 330,
            "width": 70,
            "height": 286
        },
        {
            "label": "Without_Coverall",
            "x": 166,
            "y": 333,
            "width": 86,
            "height": 293
        },
        {
            "label": "Without_Coverall",
            "x": 426,
            "y": 52,
            "width": 120,
            "height": 104
        },
        {
            "label": "Without_Coverall",
            "x": 426,
            "y": 577,
            "width": 120,
            "height": 126
        },
        {
            "label": "Without_Coverall",
            "x": 246,
            "y": 46.5,
            "width": 70,
            "height": 93
        },
        {
            "label": "Without_Coverall",
            "x": 246,
            "y": 583.5,
            "width": 70,
            "height": 113
        },
        {
            "label": "Without_Coverall",
            "x": 166,
            "y": 46.5,
            "width": 86,
            "height": 93
        },
        {
            "label": "Without_Coverall",
            "x": 166,
            "y": 580,
            "width": 86,
            "height": 120
        }
    ],
    "height": 640,
    "key": "000028.jpg",
    "width": 640
}

Coverall Detection