Dataset Versions

v27

yolov11m-640

Generated on Oct 1, 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.

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210 Total Images

View All Images

Dataset Split

Train Set 65%

136Images

Valid Set 26%

55Images

Test Set 9%

19Images

Preprocessing

Auto-Orient: Applied

Resize: Fit (grey edges) in 640x640

Filter by Tag: 1 required, 0 dropped

Augmentations

No augmentations were applied.

Similar Projects

Annotations

Group:

object

CLASSES

LAYERS

book

chair

laptop

person

Unused Classes

airplane

apple

backpack

banana

baseball bat

baseball glove

bear

bed

bench

bicycle

bird

boat

bottle

bowl

broccoli

bus

cake

car

carrot

cat

cell phone

clock

couch

cow

cup

dining table

dog

donut

elephant

fire hydrant

fork

frisbee

giraffe

hair drier

handbag

horse

hot dog

keyboard

kite

knife

microwave

motorcycle

mouse

orange

oven

parking meter

pizza

potted plant

refrigerator

remote

sandwich

scissors

sheep

sink

skateboard

skis

snowboard

spoon

sports ball

stop sign

suitcase

surfboard

teddy bear

tennis racket

tie

toaster

toilet

toothbrush

traffic light

train

truck

umbrella

vase

wine glass

zebra

000000013292.jpg

640x640

0.41MP

Updated Oct 1, 2024

9:07PM

GMT+00:00

Generated by Roboflow

Training Set

Transforms

Auto-Orient	Applied
Resize	Fit (black edges) in 640x640

Raw Data

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        "potted plant": 8995,
        "bus": 6354,
        "tv": 6093,
        "bicycle": 7427,
        "couch": 6040,
        "toothbrush": 2009,
        "cow": 8526,
        "vase": 6890,
        "laptop": 5201,
        "remote": 5985,
        "frisbee": 2797,
        "hot dog": 3045,
        "motorcycle": 9095,
        "mouse": 2368,
        "tie": 6750,
        "cake": 6663,
        "bird": 11246,
        "donut": 7517,
        "spoon": 6417,
        "fire hydrant": 1966,
        "bear": 1365,
        "train": 4761,
        "banana": 9837,
        "suitcase": 6495,
        "bench": 10251,
        "baseball bat": 3421,
        "sink": 5835,
        "stop sign": 2058,
        "clock": 6601,
        "baseball glove": 3895,
        "horse": 6860,
        "knife": 8093,
        "toilet": 4336,
        "giraffe": 5363,
        "person": 273390,
        "skis": 6886,
        "dog": 5726,
        "elephant": 5768,
        "hair drier": 209,
        "refrigerator": 2763,
        "cup": 21547,
        "snowboard": 2750,
        "skateboard": 5721,
        "surfboard": 6395,
        "book": 25876,
        "dining table": 16408,
        "truck": 10388,
        "broccoli": 7624,
        "parking meter": 1345,
        "bottle": 25367,
        "bowl": 14979,
        "cell phone": 6694,
        "apple": 6090,
        "sports ball": 6602,
        "pizza": 6106,
        "car": 45798,
        "cat": 4970,
        "toaster": 234,
        "microwave": 1728,
        "sheep": 9870,
        "zebra": 5571,
        "keyboard": 3008,
        "wine glass": 8256,
        "oven": 3476,
        "umbrella": 11844,
        "chair": 40272,
        "sandwich": 4550,
        "boat": 11189,
        "orange": 6686,
        "scissors": 1517,
        "fork": 5694,
        "handbag": 12891,
        "airplane": 5278,
        "backpack": 9090,
        "teddy bear": 4984,
        "kite": 9412,
        "tennis racket": 5037,
        "carrot": 8223,
        "traffic light": 13521
    },
    "datasets": [
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    ],
    "destination": "d11587a4234c7d92e597423b3fd5b59c",
    "height": 640,
    "id": "a02VaT08mRmNk9f9wVSk",
    "label": [],
    "labels": [],
    "name": "000000013292.jpg",
    "numSteps": 2,
    "owner": "BTRTpB7nxxjUchrOQ9vT",
    "preprocessing": [
        "auto-orient",
        "resize:[\"Fit (black edges) in\",640,640]"
    ],
    "preprocessingParsed": [
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            "value": "Applied"
        },
        {
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        }
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    "source": "a02VaT08mRmNk9f9wVSk",
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    "split.43OxjDl449AxuKXDjkMP.14": "train",
    "split.43OxjDl449AxuKXDjkMP.15": "train",
    "split.43OxjDl449AxuKXDjkMP.16": "train",
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    "split.43OxjDl449AxuKXDjkMP.21": "train",
    "split.43OxjDl449AxuKXDjkMP.22": "train",
    "split.43OxjDl449AxuKXDjkMP.23": "train",
    "split.43OxjDl449AxuKXDjkMP.24": "train",
    "split.43OxjDl449AxuKXDjkMP.25": "train",
    "split.43OxjDl449AxuKXDjkMP.26": "train",
    "split.43OxjDl449AxuKXDjkMP.27": "train",
    "split.43OxjDl449AxuKXDjkMP.28": "train",
    "split.43OxjDl449AxuKXDjkMP.29": "train",
    "split.43OxjDl449AxuKXDjkMP.30": "train",
    "split.43OxjDl449AxuKXDjkMP.31": "train",
    "split.43OxjDl449AxuKXDjkMP.32": "train",
    "split.43OxjDl449AxuKXDjkMP.33": "train",
    "split.43OxjDl449AxuKXDjkMP.34": "train",
    "status": "generated",
    "transforms": "[\n    \"auto-orient\",\n    \"resize:[\\\"Fit (black edges) in\\\",640,640]\"\n]",
    "updated": {
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        "_nanoseconds": 282000000
    },
    "updatedDate": "Oct 1, 2024",
    "updatedTime": "9:07PM",
    "updatedTimezone": "+00:00",
    "user_tags": [
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        "43OxjDl449AxuKXDjkMP/19",
        "43OxjDl449AxuKXDjkMP/20",
        "43OxjDl449AxuKXDjkMP/21",
        "43OxjDl449AxuKXDjkMP/23",
        "43OxjDl449AxuKXDjkMP/24",
        "43OxjDl449AxuKXDjkMP/22",
        "43OxjDl449AxuKXDjkMP/25",
        "43OxjDl449AxuKXDjkMP/26",
        "43OxjDl449AxuKXDjkMP/27",
        "43OxjDl449AxuKXDjkMP/28",
        "43OxjDl449AxuKXDjkMP/29",
        "43OxjDl449AxuKXDjkMP/30",
        "43OxjDl449AxuKXDjkMP/31",
        "43OxjDl449AxuKXDjkMP/33",
        "43OxjDl449AxuKXDjkMP/34",
        "43OxjDl449AxuKXDjkMP/32"
    ],
    "width": 640
}

{
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        },
        {
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}

COCO Dataset