Dataset Versions

2024-08-25 6:43am

Generated on Aug 25, 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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219 Total Images

View All Images

Dataset Split

Train Set 84%

185Images

Valid Set 16%

34Images

Test Set %

0Images

Preprocessing

Auto-Orient: Applied

Resize: Stretch to 640x640

Augmentations

Outputs per training example: 3

Grayscale: Apply to 25% of images

Similar Projects

Annotations

Group:

Shapes

3.jpg

640x640

0.41MP

Updated Sep 11, 56619

3:23AM

GMT+00:00

Generated by Roboflow

Training Set

Transforms

Auto-Orient

Applied

Resize

Stretch to 640x640

Raw Data

Source Data

                            {
    "camera": "Generated by Roboflow",
    "datasets": [
        "RVkztfhBGCGrq5d6X2mE"
    ],
    "destination": "fb1d07d05f32a2e1b0bc527cebbec0e5",
    "height": 640,
    "id": "MhlHuFLx0jl4XUMG5uF1",
    "label": [],
    "labels": [],
    "name": "3.jpg",
    "numSteps": 2,
    "owner": "N1aMZzCjVSVx9f5ebY6RxVXa0MJ3",
    "preprocessing": [
        "auto-orient",
        "resize:[\"Stretch to\",640,640]"
    ],
    "preprocessingParsed": [
        {
            "name": "Auto-Orient",
            "value": "Applied"
        },
        {
            "name": "Resize",
            "value": "Stretch to 640x640"
        }
    ],
    "source": "MhlHuFLx0jl4XUMG5uF1",
    "split": "train",
    "split.RVkztfhBGCGrq5d6X2mE.1": "train",
    "split.RVkztfhBGCGrq5d6X2mE.2": "train",
    "split.RVkztfhBGCGrq5d6X2mE.3": "train",
    "status": "generated",
    "transforms": "[\n    \"auto-orient\",\n    \"resize:[\\\"Stretch to\\\",640,640]\"\n]",
    "updated": 1724577708182,
    "updatedDate": "Sep 11, 56619",
    "updatedTime": "3:23AM",
    "updatedTimezone": "+00:00",
    "versions": [
        "RVkztfhBGCGrq5d6X2mE/1",
        "RVkztfhBGCGrq5d6X2mE/2",
        "RVkztfhBGCGrq5d6X2mE/3"
    ],
    "width": 640
}

Annotation Data

{
    "boxes": [
        {
            "label": "Triangle",
            "x": 385.5,
            "y": 183,
            "width": 139,
            "height": 88
        },
        {
            "label": "Quadrilateral",
            "x": 257,
            "y": 198,
            "width": 131,
            "height": 138
        },
        {
            "label": "Triangle",
            "x": 384,
            "y": 305.5,
            "width": 103.5,
            "height": 62
        },
        {
            "label": "Triangle",
            "x": 249,
            "y": 305.5,
            "width": 95.5,
            "height": 89
        }
    ],
    "height": 640,
    "key": "3.jpg",
    "width": 640
}

debris detection