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Object Detection and Tracking

Object Detection

Dataset Versions

Try Pre-Trained Model

2024-05-12 2:45am

Generated on May 12, 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.

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29 Total Images

View All Images

Dataset Split

Train Set 86%

25Images

Valid Set 10%

3Images

Test Set 3%

1Images

Preprocessing

Auto-Orient: Applied

Resize: Stretch to 640x640

Augmentations

Outputs per training example: 3

90° Rotate: Clockwise, Counter-Clockwise

Similar Projects

Annotations

Group:

Involvement-in-a-meeting

Crowd_wcu_mp4_mp4-0248.jpg

640x640

0.41MP

Updated Apr 4, 56332

7:38PM

GMT+00:00

Generated by Roboflow

Training Set

Transforms

Auto-Orient

Applied

Resize

Stretch to 640x640

90° Rotate

Applied

Raw Data

Source Data

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Annotation Data

{
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        {
            "label": "facce",
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            "y": 492.5,
            "width": 82,
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        },
        {
            "label": "facce",
            "x": 165.5,
            "y": 339.5,
            "width": 32,
            "height": 66
        },
        {
            "label": "Water at Bottle",
            "x": 105.5,
            "y": 355,
            "width": 16,
            "height": 80
        },
        {
            "label": "Water at Bottle",
            "x": 140.5,
            "y": 339,
            "width": 14,
            "height": 73
        },
        {
            "label": "Water at Bottle",
            "x": 206,
            "y": 280.5,
            "width": 11,
            "height": 41
        },
        {
            "label": "facce",
            "x": 196.5,
            "y": 326.5,
            "width": 26,
            "height": 70
        },
        {
            "label": "facce",
            "x": 193.5,
            "y": 287,
            "width": 34,
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        },
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            "label": "facce",
            "x": 137.5,
            "y": 262,
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        },
        {
            "label": "facce",
            "x": 74,
            "y": 285,
            "width": 31,
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        },
        {
            "label": "facce",
            "x": 32,
            "y": 318.5,
            "width": 42,
            "height": 90
        },
        {
            "label": "facce",
            "x": 8.5,
            "y": 243,
            "width": 16,
            "height": 67
        },
        {
            "label": "facce",
            "x": 17,
            "y": 207.5,
            "width": 28,
            "height": 72
        },
        {
            "label": "Water at Bottle",
            "x": 280.5,
            "y": 501.5,
            "width": 19,
            "height": 88
        },
        {
            "label": "Water at Bottle",
            "x": 289,
            "y": 443,
            "width": 14,
            "height": 41
        },
        {
            "label": "Water at Bottle",
            "x": 296,
            "y": 402,
            "width": 15,
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        },
        {
            "label": "facce",
            "x": 231,
            "y": 277.5,
            "width": 25,
            "height": 47
        },
        {
            "label": "facce",
            "x": 249,
            "y": 263.5,
            "width": 22,
            "height": 50
        },
        {
            "label": "facce",
            "x": 271.5,
            "y": 250.5,
            "width": 14,
            "height": 52
        },
        {
            "label": "facce",
            "x": 238.5,
            "y": 227,
            "width": 26,
            "height": 38
        },
        {
            "label": "facce",
            "x": 301,
            "y": 201.5,
            "width": 19,
            "height": 50
        },
        {
            "label": "facce",
            "x": 318,
            "y": 204.5,
            "width": 11,
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        },
        {
            "label": "facce",
            "x": 331,
            "y": 203.5,
            "width": 18,
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        },
        {
            "label": "facce",
            "x": 349.5,
            "y": 219.5,
            "width": 12,
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        },
        {
            "label": "facce",
            "x": 398,
            "y": 166.5,
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            "label": "facce",
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        },
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            "label": "facce",
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            "y": 259,
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        },
        {
            "label": "facce",
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            "label": "facce",
            "x": 186,
            "y": 241.5,
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            "label": "facce",
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            "y": 230,
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            "y": 239.5,
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        },
        {
            "label": "facce",
            "x": 40,
            "y": 212,
            "width": 12,
            "height": 38
        },
        {
            "label": "facce",
            "x": 121,
            "y": 221,
            "width": 14,
            "height": 56
        },
        {
            "label": "facce",
            "x": 137.5,
            "y": 219.5,
            "width": 18,
            "height": 47
        },
        {
            "label": "Flower",
            "x": 318,
            "y": 481,
            "width": 39,
            "height": 86
        },
        {
            "label": "Flower",
            "x": 331.5,
            "y": 418.5,
            "width": 42,
            "height": 78
        },
        {
            "label": "Smartboard",
            "x": 553,
            "y": 184,
            "width": 174,
            "height": 180
        },
        {
            "label": "MCI COntroller",
            "x": 456.5,
            "y": 124.5,
            "width": 22,
            "height": 116
        },
        {
            "label": "Parker",
            "x": 633,
            "y": 305.5,
            "width": 11,
            "height": 46
        },
        {
            "label": "Duster",
            "x": 472.5,
            "y": 260.5,
            "width": 15,
            "height": 32
        },
        {
            "label": "Speaker",
            "x": 253,
            "y": 35.5,
            "width": 38,
            "height": 66
        },
        {
            "label": "4k Camera",
            "x": 81,
            "y": 186,
            "width": 35,
            "height": 62
        }
    ],
    "height": 640,
    "key": "Crowd_wcu_mp4_mp4-0248.jpg",
    "width": 640
}

Object Detection and Tracking

Dataset Versions

2024-05-12 2:45am

Popular Download Formats

29 Total Images

Dataset Split

Preprocessing

Augmentations

Similar Projects

Annotations

Unused Classes

Tags

Attributes

Crowd_wcu_mp4_mp4-0248.jpg

Updated Apr 4, 56332

Generated by Roboflow

Training Set

Transforms

Annotation History

Raw Data

Source Data

Annotation Data