Dataset Viewer
Auto-converted to Parquet Duplicate
Search is not available for this dataset
image_id
int64
0
547
image
imagewidth (px)
640
640
width
int32
640
640
height
int32
640
640
objects
sequence
476
640
640
{ "id": [ 92682, 92683, 92684, 92685, 92686, 92687, 92688, 92689, 92690, 92691, 92692, 92693, 92694, 92695, 92696, 92697, 92698, 92699, 92700, 92701, 92702, 92703, 92704, 92705, 92706, 92707, 92708, 92709, ...
509
640
640
{ "id": [ 98931, 98932, 98933, 98934, 98935, 98936, 98937, 98938, 98939, 98940, 98941, 98942, 98943, 98944, 98945, 98946, 98947, 98948 ], "area": [ 34163, 24345, 145923, 24932, 23620, 1300, 1471, 9341, 2352...
107
640
640
{ "id": [ 20214, 20215, 20216, 20217, 20218, 20219, 20220, 20221, 20222, 20223, 20224, 20225, 20226, 20227, 20228, 20229, 20230, 20231, 20232, 20233, 20234, 20235, 20236, 20237, 20238, 20239, 20240, 20241, ...
408
640
640
{ "id": [ 79952, 79953, 79954, 79955, 79956, 79957, 79958, 79959, 79960, 79961, 79962, 79963, 79964, 79965, 79966, 79967, 79968, 79969, 79970, 79971, 79972, 79973, 79974, 79975, 79976, 79977, 79978, 79979, ...
359
640
640
{ "id": [ 71945, 71946, 71947, 71948, 71949, 71950, 71951, 71952, 71953, 71954, 71955, 71956, 71957, 71958, 71959, 71960, 71961, 71962, 71963, 71964, 71965, 71966, 71967, 71968, 71969, 71970, 71971, 71972, ...
465
640
640
{ "id": [ 89847, 89848, 89849, 89850, 89851, 89852, 89853, 89854, 89855, 89856, 89857, 89858, 89859, 89860, 89861, 89862, 89863, 89864, 89865, 89866, 89867, 89868, 89869, 89870, 89871, 89872, 89873, 89874, ...
87
640
640
{ "id": [ 15436, 15437, 15438, 15439, 15440, 15441, 15442, 15443, 15444, 15445, 15446, 15447, 15448, 15449, 15450, 15451, 15452, 15453, 15454, 15455, 15456, 15457, 15458, 15459, 15460, 15461, 15462, 15463, ...
54
640
640
{ "id": [ 9525, 9526, 9527, 9528, 9529, 9530, 9531, 9532, 9533, 9534, 9535, 9536, 9537, 9538, 9539, 9540, 9541, 9542, 9543, 9544, 9545, 9546, 9547, 9548, 9549, 9550, 9551, 9552, 9553, 9554, 9555...
489
640
640
{ "id": [ 95250, 95251, 95252, 95253, 95254, 95255, 95256, 95257, 95258, 95259, 95260, 95261, 95262, 95263, 95264, 95265, 95266, 95267, 95268, 95269, 95270, 95271, 95272, 95273, 95274, 95275, 95276, 95277, ...
57
640
640
{ "id": [ 10224, 10225, 10226, 10227, 10228, 10229, 10230, 10231, 10232, 10233, 10234, 10235, 10236, 10237, 10238, 10239, 10240, 10241, 10242, 10243, 10244, 10245, 10246, 10247, 10248, 10249, 10250, 10251, ...
3
640
640
{ "id": [ 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, ...
193
640
640
{ "id": [ 37469, 37470, 37471, 37472, 37473, 37474, 37475, 37476, 37477, 37478, 37479, 37480, 37481, 37482, 37483, 37484, 37485, 37486, 37487, 37488, 37489, 37490, 37491, 37492, 37493, 37494, 37495, 37496, ...
218
640
640
{ "id": [ 41779, 41780, 41781, 41782, 41783, 41784, 41785, 41786, 41787, 41788, 41789, 41790, 41791, 41792, 41793, 41794, 41795, 41796, 41797, 41798, 41799, 41800, 41801, 41802, 41803, 41804, 41805, 41806, ...
68
640
640
{ "id": [ 12021, 12022, 12023, 12024, 12025, 12026, 12027, 12028, 12029, 12030, 12031, 12032, 12033, 12034, 12035, 12036, 12037, 12038, 12039, 12040, 12041, 12042, 12043, 12044, 12045, 12046, 12047, 12048, ...
362
640
640
{ "id": [ 72546, 72547, 72548, 72549, 72550, 72551, 72552, 72553, 72554, 72555, 72556, 72557, 72558, 72559, 72560, 72561, 72562, 72563, 72564, 72565, 72566, 72567, 72568, 72569, 72570, 72571, 72572, 72573, ...
525
640
640
{ "id": [ 101863, 101864, 101865, 101866, 101867, 101868, 101869, 101870, 101871, 101872, 101873, 101874, 101875, 101876, 101877, 101878, 101879, 101880, 101881, 101882, 101883, 101884, 101885, 101886, 101887, 1018...
75
640
640
{ "id": [ 13495, 13496, 13497, 13498, 13499, 13500, 13501, 13502, 13503, 13504, 13505, 13506, 13507, 13508, 13509, 13510, 13511, 13512 ], "area": [ 42140, 35469, 177661, 33617, 29945, 1650, 1840, 11491, 302...
106
640
640
{ "id": [ 20120, 20121, 20122, 20123, 20124, 20125, 20126, 20127, 20128, 20129, 20130, 20131, 20132, 20133, 20134, 20135, 20136, 20137, 20138, 20139, 20140, 20141, 20142, 20143, 20144, 20145, 20146, 20147, ...
426
640
640
{ "id": [ 82767, 82768, 82769, 82770, 82771, 82772, 82773, 82774, 82775, 82776, 82777, 82778, 82779, 82780, 82781, 82782, 82783, 82784, 82785, 82786, 82787, 82788, 82789, 82790, 82791, 82792, 82793, 82794, ...
500
640
640
{ "id": [ 96911, 96912, 96913, 96914, 96915, 96916, 96917, 96918, 96919, 96920, 96921, 96922, 96923, 96924, 96925, 96926, 96927, 96928, 96929, 96930, 96931, 96932, 96933, 96934, 96935, 96936, 96937, 96938, ...
440
640
640
{ "id": [ 85392, 85393, 85394, 85395, 85396, 85397, 85398, 85399, 85400, 85401, 85402, 85403, 85404, 85405, 85406, 85407, 85408, 85409, 85410, 85411, 85412, 85413, 85414, 85415, 85416, 85417, 85418, 85419, ...
88
640
640
{ "id": [ 15519, 15520, 15521, 15522, 15523, 15524, 15525, 15526, 15527, 15528, 15529, 15530, 15531, 15532, 15533, 15534, 15535, 15536, 15537, 15538, 15539, 15540, 15541, 15542, 15543, 15544, 15545, 15546, ...
317
640
640
{ "id": [ 63286, 63287, 63288, 63289, 63290, 63291, 63292, 63293, 63294, 63295, 63296, 63297, 63298, 63299, 63300, 63301, 63302, 63303, 63304, 63305, 63306, 63307, 63308, 63309, 63310, 63311, 63312, 63313, ...
289
640
640
{ "id": [ 56948, 56949, 56950, 56951, 56952, 56953, 56954, 56955, 56956, 56957, 56958, 56959, 56960, 56961, 56962, 56963, 56964, 56965, 56966, 56967, 56968, 56969, 56970, 56971, 56972, 56973, 56974, 56975, ...
528
640
640
{ "id": [ 102383, 102384, 102385, 102386, 102387, 102388, 102389, 102390, 102391, 102392, 102393, 102394, 102395, 102396, 102397, 102398, 102399, 102400, 102401, 102402, 102403, 102404, 102405, 102406, 102407, 1024...
156
640
640
{ "id": [ 28671, 28672, 28673, 28674, 28675, 28676, 28677, 28678, 28679, 28680, 28681, 28682, 28683, 28684, 28685, 28686, 28687, 28688, 28689, 28690, 28691, 28692, 28693, 28694, 28695, 28696, 28697, 28698, ...
512
640
640
{ "id": [ 99393, 99394, 99395, 99396, 99397, 99398, 99399, 99400, 99401, 99402, 99403, 99404, 99405, 99406, 99407, 99408, 99409, 99410, 99411, 99412, 99413, 99414, 99415, 99416, 99417, 99418, 99419, 99420, ...
End of preview. Expand in Data Studio

Dataset Card for printed-circuit-board

** The original COCO dataset is stored at dataset.tar.gz**

Dataset Summary

printed-circuit-board

Supported Tasks and Leaderboards

  • object-detection: The dataset can be used to train a model for Object Detection.

Languages

English

Dataset Structure

Data Instances

A data point comprises an image and its object annotations.

{
  'image_id': 15,
  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
  'width': 964043,
  'height': 640,
  'objects': {
    'id': [114, 115, 116, 117], 
    'area': [3796, 1596, 152768, 81002],
    'bbox': [
      [302.0, 109.0, 73.0, 52.0],
      [810.0, 100.0, 57.0, 28.0],
      [160.0, 31.0, 248.0, 616.0],
      [741.0, 68.0, 202.0, 401.0]
    ], 
    'category': [4, 4, 0, 0]
  }
}

Data Fields

  • image: the image id
  • image: PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0]
  • width: the image width
  • height: the image height
  • objects: a dictionary containing bounding box metadata for the objects present on the image
    • id: the annotation id
    • area: the area of the bounding box
    • bbox: the object's bounding box (in the coco format)
    • category: the object's category.

Who are the annotators?

Annotators are Roboflow users

Additional Information

Licensing Information

See original homepage https://universe.roboflow.com/object-detection/printed-circuit-board

Citation Information

@misc{ printed-circuit-board,
    title = { printed circuit board Dataset },
    type = { Open Source Dataset },
    author = { Roboflow 100 },
    howpublished = { \url{ https://universe.roboflow.com/object-detection/printed-circuit-board } },
    url = { https://universe.roboflow.com/object-detection/printed-circuit-board },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { nov },
    note = { visited on 2023-03-29 },
}"

Contributions

Thanks to @mariosasko for adding this dataset.

Downloads last month
39

Models trained or fine-tuned on Francesco/printed-circuit-board