Open Images Dataset V6 News Extras Extended Download Description Explore
You are viewing the downloads of the latest version of Open Images (V6 - released Feb 2020), if you would like to view the downloads of previous versions, please select it here:

Subset with Bounding Boxes (600 classes), Object Segmentations, Visual Relationships, and Localized Narratives

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

Trouble accessing the data? Let us know.

Download and Visualize using FiftyOne

We have collaborated with the team at Voxel51 to make downloading and visualizing Open Images a breeze using their open-source tool FiftyOne.

FiftyOne Example

As with any other dataset in the FiftyOne Dataset Zoo, downloading it is as easy as calling:
dataset = fiftyone.zoo.load_zoo_dataset("open-images-v6", split="validation")
The function allows you to: These properties give you the ability to quickly download subsets of the dataset that are relevant to you.
dataset = fiftyone.zoo.load_zoo_dataset(
              "open-images-v6",
              split="validation",
              label_types=["detections", "segmentations"],
              classes=["Cat", "Dog"],
              max_samples=100,
          )
FiftyOne also provides native support for Open Images-style evaluation to compute mAP, plot PR curves, interact with confusion matrices, and explore individual label-level results.
results = dataset.evaluate_detections("predictions", gt_field="detections", method="open-images")

Download Manually

Download the images - Complete

If you're interested in downloading the full set of training, test, or validation images (annotated with bounding boxes, etc.), you can download them packaged in various compressed files from CVDF's site:

Download the images - Specific subsets

If you only need a certain subset of these images and you'd rather avoid downloading the full 1.9M images, we provide a Python script that downloads images from CVDF.
  1. Download the file downloader.py (open and press Ctrl + S), or directly run:
    wget https://raw.githubusercontent.com/openimages/dataset/master/downloader.py
  2. Create a text file containing all the image IDs that you're interested in downloading. It can come from filtering the annotations with certain classes, those annotated with a certain type of annotations (e.g., MIAP). Each line should follow the format $SPLIT/$IMAGE_ID, where $SPLIT is either "train", "test", "validation", or "challenge2018"; and $IMAGE_ID is the image ID that uniquely identifies the image. A sample file could be:
    train/f9e0434389a1d4dd
    train/1a007563ebc18664
    test/ea8bfd4e765304db
  3. Run the following script, making sure you have the dependencies installed:
    python downloader.py $IMAGE_LIST_FILE --download_folder=$DOWNLOAD_FOLDER --num_processes=5
    For help, run:
    python downloader.py -h

Download the annotations and metadata

Segmentations

* Please note that in Jan 27th 2021 we removed two entries from the visual relationships train file oidv6-train-annotations-vrd.csv because they were on image 634483a6a8a74df4, which is not in the set of images annotated with bounding boxes.

Subset with Image-Level Labels (19,958 classes)

These annotation files cover all object classes. In the train set, the human-verified labels span 7,337,077 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.

Trouble downloading the pixels? Let us know.
Human-verified labels
Machine-generated labels

Complete Open Images

The full set of 9,178,275 images.

Trouble downloading the pixels? Let us know.
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,XClick1X,XClick2X,XClick3X,XClick4X,XClick1Y,XClick2Y,XClick3Y,XClick4Y
000002b66c9c498e,xclick,/m/04bcr3,1,0.312500,0.578125,0.351562,0.464063,0,0,0,0,0,0.312500,0.578125,0.385937,0.576562,0.454688,0.364063,0.351562,0.464063
3550e6ef5d44d91a,activemil,/m/0220r2,1,0.022461,0.865234,0.234375,0.986979,1,0,0,0,0,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
880b4b00f75260ec,xclick,/m/0ch_cf,1,0.641875,0.693125,0.818333,0.878333,1,0,0,0,0,0.641875,0.643125,0.678750,0.693125,0.818333,0.842500,0.871667,0.878333
b17e3f11cb77c7c8,xclick,/m/0dzct,1,0.510156,0.664844,0.337632,0.623681,1,0,0,0,0,0.573438,0.510156,0.664844,0.587500,0.337632,0.438453,0.468933,0.623681
[...]

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

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

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