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Chess object detection

Object Detection

Dataset Versions

Try Pre-Trained Model

2024-01-29 1:28pm

Generated on Jan 29, 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.

Other Formats

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478 Total Images

View All Images

Dataset Split

Train Set 80%

383Images

Valid Set 13%

63Images

Test Set 7%

32Images

Preprocessing

Auto-Orient: Applied

Resize: Stretch to 640x640

Augmentations

Outputs per training example: 3

Flip: Horizontal

Similar Projects

Annotations

Group:

chess

Wooden-Sets-Buy-Online-Next-Day-Delivery-Great-Prices.jpeg

640x640

0.41MP

Updated Feb 20, 56047

5:57PM

GMT+00:00

Generated by Roboflow

Training Set

Transforms

Auto-Orient

Applied

Resize

Stretch to 640x640

Raw Data

Source Data

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    "camera": "Generated by Roboflow",
    "datasets": [
        "k94CS3nsoBOTm67xRWBD"
    ],
    "destination": "0d1db809402d70a33297c8a4ed87678c",
    "height": 640,
    "id": "CM9KCAvL6AYyn20cSx1R",
    "label": [],
    "labels": [],
    "name": "Wooden-Sets-Buy-Online-Next-Day-Delivery-Great-Prices.jpeg",
    "numSteps": 2,
    "owner": "qeoCYggHRcaizXJUAb11SGfdHy43",
    "preprocessing": [
        "auto-orient",
        "resize:[\"Stretch to\",640,640]"
    ],
    "preprocessingParsed": [
        {
            "name": "Auto-Orient",
            "value": "Applied"
        },
        {
            "name": "Resize",
            "value": "Stretch to 640x640"
        }
    ],
    "source": "CM9KCAvL6AYyn20cSx1R",
    "split": "train",
    "split.k94CS3nsoBOTm67xRWBD.2": "train",
    "split.k94CS3nsoBOTm67xRWBD.3": "train",
    "status": "generated",
    "transforms": "[\n    \"auto-orient\",\n    \"resize:[\\\"Stretch to\\\",640,640]\"\n]",
    "updated": 1706509706228,
    "updatedDate": "Feb 20, 56047",
    "updatedTime": "5:57PM",
    "updatedTimezone": "+00:00",
    "versions": [
        "k94CS3nsoBOTm67xRWBD/2",
        "k94CS3nsoBOTm67xRWBD/3"
    ],
    "width": 640
}

Annotation Data

{
    "boxes": [
        {
            "label": "Bidak",
            "x": 279.5,
            "y": 409.5,
            "width": 33,
            "height": 72
        },
        {
            "label": "Bidak",
            "x": 127,
            "y": 307,
            "width": 31.5,
            "height": 65.5
        },
        {
            "label": "Bidak",
            "x": 163.5,
            "y": 287.5,
            "width": 28.5,
            "height": 59
        },
        {
            "label": "Bidak",
            "x": 197,
            "y": 269.5,
            "width": 29.5,
            "height": 57.5
        },
        {
            "label": "Bidak",
            "x": 228.5,
            "y": 253.5,
            "width": 27,
            "height": 52
        },
        {
            "label": "Bidak",
            "x": 262.5,
            "y": 239.5,
            "width": 25.5,
            "height": 59
        },
        {
            "label": "Bidak",
            "x": 291,
            "y": 225,
            "width": 28,
            "height": 60
        },
        {
            "label": "Bidak",
            "x": 318.5,
            "y": 210.5,
            "width": 22,
            "height": 55.5
        },
        {
            "label": "Bidak",
            "x": 348,
            "y": 197.5,
            "width": 25.5,
            "height": 62
        },
        {
            "label": "Benteng",
            "x": 316.5,
            "y": 433.5,
            "width": 39,
            "height": 90.5
        },
        {
            "label": "Benteng",
            "x": 95.5,
            "y": 279.5,
            "width": 37.5,
            "height": 76
        },
        {
            "label": "Benteng",
            "x": 539.5,
            "y": 285,
            "width": 28.5,
            "height": 69.5
        },
        {
            "label": "Kuda",
            "x": 134,
            "y": 257.5,
            "width": 31.5,
            "height": 75
        },
        {
            "label": "Kuda",
            "x": 355.5,
            "y": 412.5,
            "width": 33,
            "height": 87
        },
        {
            "label": "Kuda",
            "x": 517,
            "y": 305,
            "width": 32,
            "height": 69.5
        },
        {
            "label": "Gajah",
            "x": 389.5,
            "y": 387.5,
            "width": 35.5,
            "height": 82.5
        },
        {
            "label": "Gajah",
            "x": 171.5,
            "y": 246.5,
            "width": 22.5,
            "height": 68.5
        },
        {
            "label": "Gajah",
            "x": 483.5,
            "y": 318.5,
            "width": 23.5,
            "height": 81.5
        },
        {
            "label": "Menteri",
            "x": 424.5,
            "y": 358,
            "width": 32,
            "height": 91.5
        },
        {
            "label": "Menteri",
            "x": 202,
            "y": 222,
            "width": 33,
            "height": 78.5
        },
        {
            "label": "Raja",
            "x": 232.5,
            "y": 204.5,
            "width": 24.5,
            "height": 91.5
        },
        {
            "label": "Raja",
            "x": 456.5,
            "y": 325,
            "width": 31.5,
            "height": 103.5
        }
    ],
    "height": 640,
    "key": "Wooden Sets - Buy Online - Next Day Delivery & Great Prices.jpeg",
    "width": 640
}

Chess object detection

Dataset Versions

2024-01-29 1:28pm

Popular Download Formats

478 Total Images

Dataset Split

Preprocessing

Augmentations

Similar Projects

Annotations

Tags

Attributes

Wooden-Sets-Buy-Online-Next-Day-Delivery-Great-Prices.jpeg

Updated Feb 20, 56047

Generated by Roboflow

Training Set

Transforms

Annotation History

Raw Data

Source Data

Annotation Data