0 dict | 1 dict | 2 dict | 3 dict | 4 dict | 5 dict | 6 dict | 7 dict | 8 dict | 9 dict | 10 dict | 11 dict | 12 dict | 13 dict | 14 dict | 15 dict | 16 dict | 17 dict | 18 dict | 19 dict | 20 dict | 21 dict | 22 dict | 23 dict | 24 dict | 25 dict | 26 dict | 27 dict | 28 dict | 29 dict | 30 dict | 31 dict | 32 dict | 33 dict | 34 dict | 35 dict | 36 dict | 37 dict | 38 dict | 39 dict | 40 dict | 41 dict | 42 dict | 43 dict | 44 dict | 45 dict | 46 dict | 47 dict | 48 dict | 49 dict | 50 dict | 51 dict | 52 dict | 53 dict | 54 dict | 55 dict | 56 dict | 57 dict | 58 dict | 59 dict | 60 dict | 61 dict | 62 dict | 63 dict | 64 dict | 65 dict | 66 dict | 67 dict | 68 dict | 69 dict | 70 dict | 71 dict | 72 dict | 73 dict | 74 dict | 75 dict | 76 dict | 77 dict | 78 dict | 79 dict | 80 dict | 81 dict | 82 dict | 83 dict | 84 dict | 85 dict | 86 dict | 87 dict | 88 dict | 89 dict | 90 dict | 91 dict | 92 dict | 93 dict | 94 dict | 95 dict | 96 dict | 97 dict | 98 dict | 99 dict | 100 dict | 101 dict | 102 dict | 103 dict | 104 dict | 105 dict | 106 dict | 107 dict | 108 dict | 109 dict | 110 dict | 111 dict | 112 dict | 113 dict | 114 dict | 115 dict | 116 dict | 117 dict | 118 dict | 119 dict | 120 dict | 121 dict | 122 dict | 123 dict | 124 dict | 125 dict | 126 dict | 127 dict | 128 dict | 129 dict | 130 dict | 131 dict | 132 dict | 133 dict | 134 dict | 135 dict | 136 dict | 137 dict | 138 dict | 139 dict | 140 dict | 141 dict | 142 dict | 143 dict | 144 dict | 145 dict | 146 dict | 147 dict | 148 dict | 149 dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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} | {
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} | {
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} | {
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} | {
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} | {
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} | {
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} | {
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"name": "sheep"
} | null | {
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"name": "car"
} | {
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"name": "bear"
} | null | null | {
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} | {
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} | {
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"name": "tie"
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"name": "suitcase"
} | null | {
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} | {
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} | null | {
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} | {
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"name": "hill"
} | null | null | null | {
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} | {
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"name": "elephant"
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"name": "painting, picture"
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} | null | null | {
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} | {
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"name": "sports ball"
} | null | null | {
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} | null | null | null | {
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} | {
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} | {
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} | {
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} | null | null | null | {
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"name": "toilet"
} | null | {
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} | null | {
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} | {
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} | {
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"name": "chandelier"
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} | null | null | {
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} | null | {
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} | null | null | null | {
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"name": "washer, automatic washer, washing machine"
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} | {
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} | null | {
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} | {
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} | null | {
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"name": "minibike, motorbike"
} | null | {
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"name": "oven"
} | {
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"name": "ball"
} | null | {
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"name": "step, stair"
} | null | {
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"name": "trade name"
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} | {
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"name": "animal"
} | null | {
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} | null | {
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} | null | {
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"name": "sculpture"
} | null | null | {
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} | {
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"name": "traffic light"
} | null | null | null | null | {
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} | {
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} | null | null | null | null |
{
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} | {
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} | {
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} | {
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"name": "horse"
} | {
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"name": "curtain"
} | {
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"name": "cow"
} | null | null | {
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"name": "zebra"
} | {
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"name": "giraffe"
} | {
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"name": "backpack"
} | null | {
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} | {
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"name": "mirror"
} | null | {
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"name": "frisbee"
} | {
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"name": "skis"
} | {
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"name": "snowboard"
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} | {
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"name": "kite"
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} | {
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} | {
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} | {
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"name": "cushion"
} | null | null | {
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"name": "fork"
} | null | {
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"name": "chest of drawers, chest, bureau, dresser"
} | null | {
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} | {
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} | {
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"name": "refrigerator, icebox"
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} | {
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"name": "cake"
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} | {
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} | {
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} | {
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"name": "bridge, span"
} | {
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"name": "bookcase"
} | null | {
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"name": "mouse"
} | {
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"name": "remote"
} | null | null | null | {
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} | {
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} | {
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"name": "stove"
} | null | null | {
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} | {
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"name": "vase"
} | {
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"name": "boat"
} | null | {
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"name": "arcade machine"
} | {
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"name": "hovel, hut, hutch, shack, shanty"
} | {
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} | null | {
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"name": "light"
} | null | {
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} | {
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"name": "door-stuff"
} | {
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"name": "floor-wood"
} | null | {
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"name": "tv"
} | {
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} | {
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} | {
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} | {
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} | {
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} | {
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} | {
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} | {
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"name": "playingfield"
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"name": "bottle"
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} | {
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} | {
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} | {
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} | {
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} | null | null | {
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} | {
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"name": "window-other"
} | {
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} | {
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} | {
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} | {
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"name": "sky-other-merged"
} | {
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"name": "cabinet-merged"
} | null | {
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"name": "tank, storage tank"
} | null | {
"isthing": 0,
"name": "mountain-merged"
} | null | {
"isthing": 0,
"name": "dirt-merged"
} | {
"isthing": 1,
"name": "bicycle"
} | null | {
"isthing": 1,
"name": "dishwasher"
} | {
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} | {
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"name": "blanket, cover"
} | {
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"name": "rug-merged"
} | {
"isthing": 1,
"name": "hood, exhaust hood"
} | null | null | null | {
"isthing": 1,
"name": "tray"
} | {
"isthing": 1,
"name": "trash can"
} | {
"isthing": 1,
"name": "fan"
} | null | null | {
"isthing": 1,
"name": "plate"
} | {
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"name": "monitor"
} | null | null | null | null | {
"isthing": 1,
"name": "clock"
} | null |
edition_3083_shi-labs-oneformer_demo-readymade
A Readymade by TheFactoryX
Original Dataset
Process
This dataset is a "readymade" - inspired by Marcel Duchamp's concept of taking everyday objects and recontextualizing them as art.
What we did:
- Selected the original dataset from Hugging Face
- Shuffled each column independently
- Destroyed all row-wise relationships
- Preserved structure, removed meaning
The result: Same data. Wrong order. New meaning. No meaning.
Purpose
This is art. This is not useful. This is the point.
Column relationships have been completely destroyed. The data maintains its types and values, but all semantic meaning has been removed.
Part of the Readymades project by TheFactoryX.
"I am a machine." — Andy Warhol
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