Open Images Dataset V5 News Extras Extended Download Description Explore
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Subset with Bounding Boxes (600 classes), Object Segmentations, and Visual Relationships

These annotation files cover the 600 boxable object classes, and span the 1,743,042 training images where we annotated bounding boxes, object segmentations, and visual relationships, as well as the full validation (41,620 images) and test (125,436 images) sets.

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Segmentations

Subset with Image-Level Labels (19,959 classes)

These annotation files cover all object classes. In the train set, the human-verified labels span 6,287,678 images, while the machine-generated labels span 8,949,445 images.
The image IDs below list all images that have human-verified labels.
The annotation files span the full validation (41,620 images) and test (125,436 images) sets.

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Human-verified labels
Machine-generated labels

Complete Open Images

The full set of 9,178,275 images.

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Image IDs

Open Images Extended

Crowdsourced

Data Formats

Bounding boxes

Each row defines one bounding box.

ImageID,Source,LabelName,Confidence,XMin,XMax,YMin,YMax,IsOccluded,IsTruncated,IsGroupOf,IsDepiction,IsInside
0001eeaf4aed83f9,xclick,/m/0cmf2,1,0.022673031,0.9642005,0.07103825,0.80054647,0,0,0,0,0
000595fe6fee6369,xclick,/m/02xwb,1,0.45655376,0.6097202,0.20399113,0.50554323,0,0,1,0,0
00075905539074f2,xclick,/m/04yx4,1,0.020477816,0.32935154,0.0956023,0.665392,0,0,0,1,0
000a1249af2bc5f0,xclick,/m/09j2d,1,0.56911767,0.99852943,0.0022172949,0.93569845,1,1,0,0,0
...


The attributes have the following definitions:

For each of them, value 1 indicates present, 0 not present, and -1 unknown.

Instance segmentation masks

The masks information is stored in two files:

The masks images are PNG binary images, where non-zero pixels belong to a single object instance and zero pixels are background. The file names look as follows (random 5 examples):

e88da03f2d80f1a1_m019jd_e16d01b9.png
540c5536e95a3282_m014j1m_b00fa52e.png
1c84bdd61fa3b883_m06m11_62ef2388.png
663389d2c9d562d8_m04_sv_7e23f2a5.png
072b8fd82919ab3e_m06mf6_dd70f221.png

The format of .zip archives names is the following: each <subset>_<suffix>.zip contains all masks for all images with the first characted of ImageID equal to <suffix>.
The value of <suffix> is from 0-9 and a-f.

Each row in masks_data.csv describes one instance, using similar conventions as the boxes CSV data file.

MaskPath,ImageID,LabelName,BoxID,BoxXMin,BoxXMax,BoxYMin,BoxYMax,PredictedIoU,Clicks
25adb319ebc72921_m02mqfb_8423aba8.png,25adb319ebc72921,/m/02mqfb,8423aba8,0.000000,0.998438,0.089062,0.770312,0.62821,0.15808 0.26206 1;0.90333 0.41076 0;0.17578 0.66566 1;0.00761 0.23197 1;0.07918 0.26058 0;0.31792 0.47737 1;0.12858 0.59262 0;0.73229 0.34016 1;0.01865 0.20001 1;0.52214 0.31037 0;0.83596 0.28105 1;0.23418 0.60177 0
0a419be97dec2fa3_m02mqfb_8ad2c442.png,0a419be97dec2fa3,/m/02mqfb,8ad2c442,0.057813,0.943750,0.056250,0.960938,0.87836,0.89971 0.08481 1;0.20175 0.90471 0;0.11511 0.89990 0;0.94728 0.28410 0;0.19611 0.85369 0;0.07672 0.87857 1;0.82215 0.62642 0;0.13916 0.92650 1;0.51738 0.48419 1
8eef6e54789ce66d_m02mqfb_83dae39c.png,8eef6e54789ce66d,/m/02mqfb,83dae39c,0.037500,0.978750,0.129688,0.925000,0.70206,0.40219 0.16838 1;0.56758 0.65286 1;0.08311 0.90762 1;0.20840 0.56515 1;0.43336 0.23679 0;0.24689 0.43426 0;0.49292 0.65762 1;0.31383 0.51431 0;0.07137 0.86214 0;0.68160 0.38210 1;0.69462 0.59568 0
...

Visual relationships

Each row in the file corresponds to a single annotation.

ImageID,LabelName1,LabelName2,XMin1,XMax1,YMin1,YMax1,XMin2,XMax2,YMin2,YMax2,RelationLabel
0009fde62ded08a6,/m/0342h,/m/01d380,0.2682927,0.78549093,0.4977778,0.8288889,0.2682927,0.78549093,0.4977778,0.8288889,is
00198353ef684011,/m/01mzpv,/m/04bcr3,0.23779725,0.30162704,0.6500938,0.7335835,0,0.5819775,0.6482176,0.99906194,at
001e341dd7456c72,/m/04yx4,/m/01mzpv,0.07009346,0.2859813,0.2332708,0.5203252,0.14018692,0.31588784,0.32082552,0.48405254,on
001e341dd7456c72,/m/04yx4,/m/01mzpv,0,0.28317758,0.26454034,0.5540963,0.2224299,0.3411215,0.3908693,0.4859287,on
001e341dd7456c72,/m/01599,/m/04bcr3,0.5551402,0.6084112,0.50343966,0.5490932,0.5411215,0.95981306,0.5090682,0.78361475,on
001e341dd7456c72,/m/04bcr3,/m/01d380,0.7392523,0.9990654,0.3889931,0.518449,0.7392523,0.9990654,0.3889931,0.518449,is
...

Image Labels

Human-verified and machine-generated image-level labels:

ImageID,Source,LabelName,Confidence
000026e7ee790996,verification,/m/04hgtk,0
000026e7ee790996,verification,/m/07j7r,1
000026e7ee790996,crowdsource-verification,/m/01bqvp,1
000026e7ee790996,crowdsource-verification,/m/0csby,1
000026e7ee790996,verification,/m/01_m7,0
000026e7ee790996,verification,/m/01cbzq,1
000026e7ee790996,verification,/m/01czv3,0
000026e7ee790996,verification,/m/01v4jb,0
000026e7ee790996,verification,/m/03d1rd,0
...

Source: indicates how the annotation was created:

Confidence: Labels that are human-verified to be present in an image have confidence = 1 (positive labels). Labels that are human-verified to be absent from an image have confidence = 0 (negative labels). Machine-generated labels have fractional confidences, generally >= 0.5. The higher the confidence, the smaller the chance for the label to be a false positive.

Class Names

The class names in MID format can be converted to their short descriptions by looking into class-descriptions.csv:

...
/m/0pc9,Alphorn
/m/0pckp,Robin
/m/0pcm_,Larch
/m/0pcq81q,Soccer player
/m/0pcr,Alpaca
/m/0pcvyk2,Nem
/m/0pd7,Army
/m/0pdnd2t,Bengal clockvine
/m/0pdnpc9,Bushwacker
/m/0pdnsdx,Enduro
/m/0pdnymj,Gekkonidae
...

Note the presence of characters like commas and quotes. The file follows standard CSV escaping rules. e.g.:

/m/02wvth,"Fiat 500 ""topolino"""
/m/03gtp5,Lamb's quarters
/m/03hgsf0,"Lemon, lime and bitters"

Image IDs

It has image URLs, their OpenImages IDs, the rotation information, titles, authors, and license information:

ImageID,Subset,OriginalURL,OriginalLandingURL,License,AuthorProfileURL,Author,Title,
OriginalSize,OriginalMD5,Thumbnail300KURL,Rotation
...
000060e3121c7305,train,https://c1.staticflickr.com/5/4129/5215831864_46f356962f_o.jpg,\
https://www.flickr.com/photos/brokentaco/5215831864,\
https://creativecommons.org/licenses/by/2.0/,\
"https://www.flickr.com/people/brokentaco/","David","28 Nov 2010 Our new house."\
211079,0Sad+xMj2ttXM1U8meEJ0A==,https://c1.staticflickr.com/5/4129/5215831864_ee4e8c6535_z.jpg,0
...

Each image has a unique 64-bit ID assigned. In the CSV files they appear as zero-padded hex integers, such as 000060e3121c7305.

The data is as it appears on the destination websites.

Hierarchy for 600 boxable classes

View the set of boxable classes as a hierarchy here or download it as a JSON file:

Hierarchy Visualizer

References

  1. "We don't need no bounding-boxes: Training object class detectors using only human verification, Papadopolous et al., CVPR 2016.

  2. "Extreme clicking for efficient object annotation", Papadopolous et al., ICCV 2017.

  3. "Large-scale interactive object segmentation with human annotators", Benenson et al., CVPR 2019.