Dataset Versions

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2024-09-30 4:25pm

2024-09-26 4:16pm

2024-09-25 9:00am

2024-09-22 2:51pm

2024-09-22 12:39pm

v14

2024-09-30 4:25pm

Generated on Sep 30, 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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534 Total Images

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Dataset Split

Train Set 70%

374Images

Valid Set 20%

106Images

Test Set 10%

54Images

Preprocessing

Auto-Orient: Applied

Resize: Stretch to 640x640

Augmentations

No augmentations were applied.

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Red macro

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8 / 12

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valid

Labels

Attributes

Raw Data

Annotations

Group:

things-on-screen

CLASSES

LAYERS

Dark-flame

1

Precise-mark

1

Saturator

1

Target-practice

4

Unused Classes

Activated-target

Falling-star

Flame

Honey-mark

Pollen-mark

Precise-target

Regular-target

Smiley-token

Tags

No Tags Applied

Type and select tags below to add them to the image.

Attributes

frame_116.png

640x640

0.41MP

Updated Sep 30, 2024

4:25PM

GMT+00:00

Generated by Roboflow

Validation Set

Transforms

Auto-Orient	Applied
Resize	Stretch to 640x640

Annotation History

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Raw Data

{
    "camera": "Generated by Roboflow",
    "classes": {
        "Pollen-mark": 714,
        "Honey-mark": 325,
        "Smiley-token": 54,
        "Flame": 2134,
        "Activated-target": 271,
        "Regular-target": 1173,
        "Precise-mark": 490,
        "Dark-flame": 1008,
        "Falling-star": 135,
        "Precise-target": 445,
        "Saturator": 458,
        "Target-practice": 1321
    },
    "datasets": [
        "T6YfoChbNPNlVgi5wv83"
    ],
    "destination": "50b40b880fd3d23fbe71dc195d56c709",
    "height": 640,
    "id": "CZtRyEq1IUd61Vn318Up",
    "label": [],
    "labels": [],
    "name": "frame_116.png",
    "numSteps": 2,
    "owner": "OjVuihKVAhWW0h39iFpskhPQvVS2",
    "preprocessing": [
        "auto-orient",
        "resize:[\"Stretch to\",640,640]"
    ],
    "preprocessingParsed": [
        {
            "name": "Auto-Orient",
            "value": "Applied"
        },
        {
            "name": "Resize",
            "value": "Stretch to 640x640"
        }
    ],
    "source": "CZtRyEq1IUd61Vn318Up",
    "split": "valid",
    "split.T6YfoChbNPNlVgi5wv83.10": "valid",
    "split.T6YfoChbNPNlVgi5wv83.11": "valid",
    "split.T6YfoChbNPNlVgi5wv83.12": "valid",
    "split.T6YfoChbNPNlVgi5wv83.13": "valid",
    "split.T6YfoChbNPNlVgi5wv83.14": "valid",
    "split.T6YfoChbNPNlVgi5wv83.8": "valid",
    "split.T6YfoChbNPNlVgi5wv83.9": "valid",
    "status": "generated",
    "transforms": "[\n    \"auto-orient\",\n    \"resize:[\\\"Stretch to\\\",640,640]\"\n]",
    "updated": {
        "_seconds": 1727713549,
        "_nanoseconds": 851000000
    },
    "updatedDate": "Sep 30, 2024",
    "updatedTime": "4:25PM",
    "updatedTimezone": "+00:00",
    "versions": [
        "T6YfoChbNPNlVgi5wv83/10",
        "T6YfoChbNPNlVgi5wv83/13",
        "T6YfoChbNPNlVgi5wv83/14"
    ],
    "width": 640
}

{
    "boxes": [
        {
            "label": "Target-practice",
            "x": 19,
            "y": 206.5,
            "width": 22,
            "height": 29.5
        },
        {
            "label": "Dark-flame",
            "x": 383,
            "y": 233,
            "width": 10,
            "height": 19.5
        },
        {
            "label": "Saturator",
            "x": 187,
            "y": 238.5,
            "width": 24,
            "height": 56
        },
        {
            "label": "Target-practice",
            "x": 66.5,
            "y": 242,
            "width": 24.5,
            "height": 35.5
        },
        {
            "label": "Target-practice",
            "x": 90.5,
            "y": 287.5,
            "width": 24.5,
            "height": 31.5
        },
        {
            "label": "Target-practice",
            "x": 15.5,
            "y": 316,
            "width": 31,
            "height": 37.5
        },
        {
            "label": "Precise-mark",
            "x": 317.5,
            "y": 170,
            "width": 25.5,
            "height": 53
        }
    ],
    "height": 640,
    "key": "frame_116.png",
    "width": 640
}

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