Datasets:
Tags:
License:
id
int64 99.1M
176M
| image
imagewidth (px) 2.71k
2.8k
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listlengths 0
16
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152,900,007 | [
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113,400,078 | [] |
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149,000,045 | [
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162,300,083 | [] |
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0.110352
],
"label": "Live_Knot"
}
] |
|
145,000,055 | [
{
"bb": [
0.503393,
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0.023928,
0.945313
],
"label": "Crack"
},
{
"bb": [
0.697321,
0.422363,
0.028215,
0.05957
],
"label": "Dead_Knot"
}
] |
|
142,100,045 | [
{
"bb": [
0.709107,
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],
"label": "Dead_Knot"
},
{
"bb": [
0.472857,
0.131836,
0.02,
0.042968
],
"label": "Dead_Knot"
}
] |
|
109,200,012 | [
{
"bb": [
0.468928,
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],
"label": "Live_Knot"
},
{
"bb": [
0.575715,
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0.044922
],
"label": "Live_Knot"
},
{
"bb": [
0.786607,
0.131836,
0.176786,
0.146484
],
"label": "Live_Knot"
}
] |
|
155,400,028 | [
{
"bb": [
0.492321,
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],
"label": "Crack"
},
{
"bb": [
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],
"label": "Crack"
},
{
"bb": [
0.479822,
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],
"label": "Crack"
},
{
"bb": [
0.703392,
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0.046875
],
"label": "Dead_Knot"
}
] |
|
153,500,003 | [
{
"bb": [
0.53625,
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],
"label": "Dead_Knot"
}
] |
|
156,600,014 | [
{
"bb": [
0.67,
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],
"label": "resin"
},
{
"bb": [
0.707143,
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],
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},
{
"bb": [
0.349464,
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],
"label": "Live_Knot"
}
] |
|
118,300,055 | [
{
"bb": [
0.402678,
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0.158215,
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],
"label": "knot_with_crack"
},
{
"bb": [
0.705715,
0.07959,
0.138571,
0.145508
],
"label": "knot_with_crack"
}
] |
|
162,300,082 | [
{
"bb": [
0.844821,
0.803222,
0.0525,
0.057617
],
"label": "Dead_Knot"
}
] |
|
128,700,045 | [
{
"bb": [
0.31,
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],
"label": "Live_Knot"
},
{
"bb": [
0.433571,
0.772461,
0.027857,
0.054688
],
"label": "Live_Knot"
}
] |
|
141,400,024 | [
{
"bb": [
0.364643,
0.709472,
0.029286,
0.063477
],
"label": "Dead_Knot"
}
] |
|
106,500,053 | [
{
"bb": [
0.509642,
0.741699,
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],
"label": "Crack"
},
{
"bb": [
0.509107,
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0.016072,
0.115235
],
"label": "Crack"
},
{
"bb": [
0.5,
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0.021428,
0.349609
],
"label": "Crack"
},
{
"bb": [
0.371071,
0.142578,
0.025,
0.042968
],
"label": "Dead_Knot"
}
] |
|
104,800,024 | [
{
"bb": [
0.369822,
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0.001071,
0.004882
],
"label": "Dead_Knot"
},
{
"bb": [
0.243928,
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],
"label": "Dead_Knot"
},
{
"bb": [
0.583036,
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],
"label": "Live_Knot"
},
{
"bb": [
0.463214,
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0.018571,
0.042969
],
"label": "Live_Knot"
}
] |
|
171,100,080 | [
{
"bb": [
0.310179,
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],
"label": "resin"
},
{
"bb": [
0.475357,
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],
"label": "Live_Knot"
},
{
"bb": [
0.330178,
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0.076071,
0.081055
],
"label": "Live_Knot"
}
] |
|
130,500,076 | [
{
"bb": [
0.881428,
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],
"label": "Dead_Knot"
},
{
"bb": [
0.300357,
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],
"label": "Dead_Knot"
}
] |
|
123,600,021 | [
{
"bb": [
0.168929,
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0.019285,
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],
"label": "resin"
},
{
"bb": [
0.091071,
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0.01,
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],
"label": "resin"
},
{
"bb": [
0.385714,
0.300293,
0.020714,
0.358398
],
"label": "Crack"
}
] |
|
175,300,077 | [
{
"bb": [
0.50875,
0.346191,
0.039642,
0.692383
],
"label": "Marrow"
},
{
"bb": [
0.920715,
0.108886,
0.022857,
0.043945
],
"label": "Knot_missing"
}
] |
|
117,900,037 | [
{
"bb": [
0.525536,
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0.063477
],
"label": "Dead_Knot"
},
{
"bb": [
0.0925,
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0.045714,
0.052734
],
"label": "Dead_Knot"
}
] |
|
150,800,063 | [] |
|
155,000,024 | [
{
"bb": [
0.480893,
0.384765,
0.016072,
0.769531
],
"label": "Crack"
},
{
"bb": [
0.742679,
0.86377,
0.100357,
0.172851
],
"label": "Dead_Knot"
}
] |
|
161,500,021 | [
{
"bb": [
0.925714,
0.544434,
0.015714,
0.071289
],
"label": "Knot_missing"
},
{
"bb": [
0.3525,
0.639161,
0.038572,
0.057617
],
"label": "Dead_Knot"
},
{
"bb": [
0.624286,
0.72168,
0.031429,
0.064453
],
"label": "Live_Knot"
}
] |
|
144,500,009 | [
{
"bb": [
0.589286,
0.017578,
0.033571,
0.035156
],
"label": "Live_Knot"
}
] |
|
104,900,034 | [
{
"bb": [
0.31875,
0.652832,
0.0225,
0.18457
],
"label": "resin"
}
] |
|
131,000,046 | [
{
"bb": [
0.647321,
0.801758,
0.016071,
0.052734
],
"label": "resin"
},
{
"bb": [
0.626964,
0.792969,
0.025357,
0.177734
],
"label": "resin"
},
{
"bb": [
0.242857,
0.121094,
0.021428,
0.242187
],
"label": "resin"
},
{
"bb": [
0.496071,
0.483886,
0.04,
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],
"label": "Live_Knot"
},
{
"bb": [
0.417857,
0.479492,
0.045,
0.070312
],
"label": "Live_Knot"
}
] |
|
171,500,019 | [
{
"bb": [
0.322858,
0.609375,
0.077143,
0.132812
],
"label": "Live_Knot"
}
] |
|
170,900,075 | [
{
"bb": [
0.862858,
0.62207,
0.147857,
0.199219
],
"label": "Live_Knot"
}
] |
|
169,300,001 | [] |
|
123,800,039 | [
{
"bb": [
0.146964,
0.956055,
0.095357,
0.060547
],
"label": "Dead_Knot"
},
{
"bb": [
0.205536,
0.263672,
0.043929,
0.042969
],
"label": "Dead_Knot"
},
{
"bb": [
0.413393,
0.575684,
0.0525,
0.067383
],
"label": "Live_Knot"
}
] |
|
141,100,077 | [
{
"bb": [
0.411607,
0.5,
0.1025,
1
],
"label": "Marrow"
},
{
"bb": [
0.906071,
0.232911,
0.055,
0.036133
],
"label": "Dead_Knot"
},
{
"bb": [
0.488036,
0.252441,
0.1275,
0.061523
],
"label": "Live_Knot"
}
] |
|
162,600,030 | [
{
"bb": [
0.07,
0.601562,
0.037858,
0.796875
],
"label": "Blue_Stain"
}
] |
|
150,400,061 | [
{
"bb": [
0.226428,
0.746582,
0.163571,
0.176758
],
"label": "knot_with_crack"
},
{
"bb": [
0.390357,
0.081055,
0.030714,
0.083985
],
"label": "Dead_Knot"
}
] |
|
144,100,052 | [
{
"bb": [
0.521786,
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0.064453
],
"label": "Dead_Knot"
},
{
"bb": [
0.521428,
0.123047,
0.017857,
0.048828
],
"label": "Dead_Knot"
}
] |
|
111,500,061 | [
{
"bb": [
0.623929,
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0.143571,
0.131836
],
"label": "Live_Knot"
},
{
"bb": [
0.336964,
0.149414,
0.101786,
0.136718
],
"label": "Live_Knot"
},
{
"bb": [
0.526428,
0.065918,
0.092857,
0.124024
],
"label": "Live_Knot"
}
] |
|
116,900,066 | [
{
"bb": [
0.306608,
0.780273,
0.020357,
0.107422
],
"label": "resin"
},
{
"bb": [
0.349107,
0.691894,
0.026786,
0.223633
],
"label": "resin"
},
{
"bb": [
0.31875,
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0.222656
],
"label": "resin"
},
{
"bb": [
0.748214,
0.048829,
0.019286,
0.091797
],
"label": "resin"
},
{
"bb": [
0.697858,
0.825684,
0.162857,
0.157227
],
"label": "knot_with_crack"
},
{
"bb": [
0.327857,
0.977539,
0.048572,
0.044922
],
"label": "Dead_Knot"
},
{
"bb": [
0.2675,
0.878906,
0.016428,
0.044922
],
"label": "Dead_Knot"
}
] |
End of preview. Expand
in Dataset Viewer.
Kodytek P, Bodzas A and Bilik P. A large-scale image dataset of wood surface defects for automated vision-based quality control processes [version 2; peer review: 2 approved]. F1000Research 2022, 10:581 (https://doi.org/10.12688/f1000research.52903.2)
Bounding boxes only, semantic maps
All images are 2800 x 1024 pixels (width x height)
Images compressed using
PIL.Image.Image.save(
format="JPEG",
optimize=True,
quality=50,
)
Bounding boxes converted to YOLO format.
TODO: loader script, preview, semantic maps
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