Dataset Versions

v10

2024-10-08 1:41pm

Generated on Oct 8, 2024

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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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550 Total Images

View All Images

Dataset Split

Train Set 98%

540Images

Valid Set 1%

7Images

Test Set 1%

3Images

Preprocessing

Resize: Stretch to 480x480

Augmentations

Outputs per training example: 3

Crop: 0% Minimum Zoom, 27% Maximum Zoom

Rotation: Between -15° and +15°

Shear: ±10° Horizontal, ±14° Vertical

Noise: Up to 4.73% of pixels

Cutout: 5 boxes with 16% size each

Similar Projects

Annotations

Group:

Cross

CLASSES

LAYERS

02.jpeg

480x480

0.23MP

Updated Oct 8, 2024

1:41PM

GMT+00:00

Generated by Roboflow

Training Set

Transforms

Crop	Keep 96% Centered on 11%, 11%
Resize	Stretch to 480x480
Rotation	0°
Shear	X: -2° Y: 12°
Noise	3.64%
Cutout	Applied

Raw Data

{
    "camera": "Generated by Roboflow",
    "classes": {
        "0": 150
    },
    "datasets": [
        "ssgbhtH788HhFlwQ3uvd"
    ],
    "destination": "30a43f38e0a736aeaea7ce5b3e829c6d",
    "height": 480,
    "id": "234OgRUpsOkDptPkEH99",
    "label": [],
    "labels": [],
    "name": "02.jpeg",
    "numSteps": 6,
    "owner": "MeWW7Qz1ocexCp8Xjbxn5cOvSBj2",
    "preprocessing": [
        "crop:[96,0.107733,0.108026]",
        "resize:[\"Stretch to\",480,480]",
        "rotate:[0]",
        "shear:[-2,12]",
        "noise:[3.64,740308]",
        "cutout:[{\"masks\":[[16,0.4982688458594693,0.10233865316134239],[16,0.4381376605409264,0.26433910880695666],[16,0.7309001128272479,0.9138442358347523],[16,0.4903322679087134,0.5996902053936868],[16,0.4786080860164599,0.971440804982951]]}]"
    ],
    "preprocessingParsed": [
        {
            "name": "Crop",
            "value": "Keep 96%<br />Centered on 11%, 11%"
        },
        {
            "name": "Resize",
            "value": "Stretch to 480x480"
        },
        {
            "name": "Rotation",
            "value": "0°"
        },
        {
            "name": "Shear",
            "value": "X: -2°<br/>Y: 12°"
        },
        {
            "name": "Noise",
            "value": "3.64%"
        },
        {
            "name": "Cutout",
            "value": "Applied"
        }
    ],
    "source": "234OgRUpsOkDptPkEH99",
    "split": "train",
    "split.ssgbhtH788HhFlwQ3uvd.10": "train",
    "status": "generated",
    "transforms": "[\n    \"crop:[96,0.107733,0.108026]\",\n    \"resize:[\\\"Stretch to\\\",480,480]\",\n    \"rotate:[0]\",\n    \"shear:[-2,12]\",\n    \"noise:[3.64,740308]\",\n    \"cutout:[{\\\"masks\\\":[[16,0.4982688458594693,0.10233865316134239],[16,0.4381376605409264,0.26433910880695666],[16,0.7309001128272479,0.9138442358347523],[16,0.4903322679087134,0.5996902053936868],[16,0.4786080860164599,0.971440804982951]]}]\"\n]",
    "updated": {
        "_seconds": 1728394897,
        "_nanoseconds": 540000000
    },
    "updatedDate": "Oct 8, 2024",
    "updatedTime": "1:41PM",
    "updatedTimezone": "+00:00",
    "versions": [
        "ssgbhtH788HhFlwQ3uvd/10"
    ],
    "width": 480
}

{
    "boxes": [
        {
            "label": "0",
            "x": 456.2426056269976,
            "y": 226.0614432760995,
            "width": 44.90831276892982,
            "height": 167.00685291357092
        }
    ],
    "height": 480,
    "key": "02.jpeg",
    "width": 480
}

CBR-CrossDetection