![](https://cdn-avatars.huggingface.co/v1/production/uploads/64006c09330a45b03605bba3/FvdxiTkTqH8rKDOzGZGUE.jpeg)
Datasets:
image_url
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http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800 | a very typical bus station |
http://78.media.tumblr.com/3b133294bdc7c7784b781b45eb9af7be/tumblr_nbirmjpEme1tkk0fco1_500.jpg | sierra looked stunning in this top and this skirt while performing with person at their former university |
https://media.gettyimages.com/photos/young-confused-girl-standing-in-front-of-a-wardrobe-picture-id511063329?s=612x612 | young confused girl standing in front of a wardrobe |
interior design of modern living room with fireplace in a new house |
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cybernetic scene isolated on white background . |
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https://media.gettyimages.com/photos/jayz-attends-the-chicago-bulls-vs-brooklyn-nets-playoff-game-at-the-picture-id167112882 | gangsta rap artist attends sports team vs playoff game in the borough . |
the jetty : different types of plants to establish a variety of ecosystems . |
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traditional ornamental floral paisley bandanna . |
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https://media.gettyimages.com/photos/bryan-mccabe-of-the-toronto-maple-leafs-skates-against-the-new-jersey-picture-id73480619?k=6&m=73480619&s=612x612&w=0&h=99aa-OK9NQ_tJCllsuA5vifiFfxD-EW13TNYlg9m9D8= | # of the sports team skates against sports team during their game . |
http://www.robinhoodshow.com/clients/17668/8642054_org.jpg | by geographical feature category or in the city - a dome for every environment |
http://i.dailymail.co.uk/i/pix/2016/08/10/15/371349E500000578-0-image-a-15_1470837871650.jpg | a flight was traveling when the animal got free on tuesday night |
even though agricultural conditions are not ideal for growing tobacco , there is indigenous production . |
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http://image.dailyfreeman.com/storyimage/DF/20170505/NEWS/170509808/AR/0/AR-170509808.jpg&maxh=400&maxw=667 | us state speaks during a demonstration thursday . |
https://media.gettyimages.com/photos/actor-john-cothran-jr-arrives-for-the-premiere-of-the-film-black-in-picture-id539614492?s=612x612 | actor arrives for the premiere of the film |
http://images.gmanews.tv/webpics/2016/11/640_2_2016_11_29_17_19_15.jpg | celebrities start decorating for the christmas season lifestyle |
http://images.slideplayer.com/2/765769/slides/slide_46.jpg | functions of government : 1 . form a more perfect union |
https://media.gettyimages.com/photos/actress-kirsten-dunst-attends-the-premiere-of-fxs-fargo-season-2-at-picture-id491859790?s=612x612 | actor attends the premiere of season |
http://www.bostonherald.com/sites/default/files/styles/gallery/public/media/2016/08/15/081516patsnl15.jpg?itok=ka5cQo6t | american football player on the field during joint training camp . |
http://globe-views.com/dcim/dreams/court/court-04.jpg | companies have gone to court for the right to lie |
all shots by by person and rider shots can be found on his website . |
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http://2.bp.blogspot.com/-cZpqM2kr2uc/U6v-pnIxM9I/AAAAAAAAaek/OFq7OBbqkv4/s1600/photo-of-a-deer-with-a-wildfire-in-the-background-hd-deer-wallpapers.jpg | photo of a deer and wildfire |
https://media.gettyimages.com/photos/high-angle-view-of-a-businessman-lying-on-a-table-and-singing-picture-id56677061?s=612x612 | high angle view of a businessman lying on a table and singing |
this is real fast food ! |
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safe deposit with money around it on a white background photo |
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the giraffe before he was shot dead then autopsied in the presence of the zoo 's visitors , despite an online petition to save him signed by thousands of animal lovers |
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http://www.golfeurope.com/photo-galleries/images/preview/39779.jpg | dunes lay the blueprint for the back nine . |
http://l7.alamy.com/zooms/7f4ac7c33e5841b6a94a35915e5ca1d7/portrait-of-a-smiling-woman-stroking-her-dog-lying-on-couch-d9g1bc.jpg | portrait of a smiling woman stroking her dog lying on couch |
http://l7.alamy.com/zooms/b7385d563da542c2be31a30be3a069a6/young-business-woman-on-a-bench-g0dktp.jpg | young business woman on a bench |
http://img.bleacherreport.net/img/images/photos/003/558/542/hi-res-25da6f4fff0b052f7c72a5af985559ff_crop_north.jpg?h=533&w=800&q=70&crop_x=center&crop_y=top | american football player looks downfield during the second half of a football game against sports team |
http://davidbarrie.typepad.com/.a/6a00d834519d9469e2013485c5657d970c-800wi | ... and local people to deliver a new bridge |
https://media.gettyimages.com/photos/actress-kate-hudson-arrives-to-the-premiere-of-lionsgates-my-best-picture-id82832165?s=612x612 | actor arrives to the premiere |
http://4.bp.blogspot.com/-NTT96j0rYZ0/U8lsad0ruCI/AAAAAAABBMU/LKmGZp-Hsg8/s1600/funny-animals-117-40.jpg | funny animals of the week , animal pictures |
see the inspiring way this woman documented her travels on her prosthetic leg |
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http://www.brian-coffee-spot.com/wp-content/uploads/wow-slider-plugin/555/images/dsc_0838.jpg | the sign promises as much as the glorious blue sky . |
https://media.gettyimages.com/photos/architectural-details-of-a-bridge-picture-id511832249?s=612x612 | architectural details of a bridge |
http://l7.alamy.com/zooms/a4197bf168734006b10e02a8bebdd34e/people-tour-and-enjoy-the-high-line-public-park-in-new-york-city-during-ga67w4.jpg | people tour and enjoy the public park during summer |
interesting 1930 's poster for a cosmetic company with stores . |
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http://cdn.newsapi.com.au/image/v1/af347cd6569987c49a71fe05870753c5?width=1024 | racecar driver steers his car during video game subject . |
vintage elegant floral card with frame decorated with black and white lilies on a pink background . |
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heavy snow falls over a snow lined river . |
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http://homeklondike.org/wp-content/uploads/2015/06/2-Bright-living-room-in-the-attic1.jpg | bright living room in the attic |
https://media.gettyimages.com/photos/actor-kevin-mchale-attends-the-3rd-annual-nautica-oceana-beach-house-picture-id472759470?s=612x612 | pop artist attends the 3rd annual at guest house |
illustration of a map , its flag and a comic balloon with a soccer ball in a not allowed signal |
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https://media.gettyimages.com/photos/musician-sting-performs-on-stage-at-the-46th-annual-grammy-awards-at-picture-id2949702?s=612x612 | rock artist performs on stage at awards held |
green sea turtle isolated on a white background 3d illustration |
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http://i.dailymail.co.uk/i/pix/2017/07/27/17/42B4031600000578-0-image-m-9_1501174323232.jpg | person , was surprised by the staff |
red and white flag on the mast |
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http://ichef.bbci.co.uk/onesport/cps/624/cpsprodpb/13A58/production/_89827408_kiss.jpg | football player celebrates scoring for football team against football team in the final |
http://l7.alamy.com/zooms/b365ec4718354a5bab1eb4413bbb94ae/detroit-michigan-the-vw-cross-coup-gte-concept-plug-in-hybrid-car-ef27pw.jpg | concept plug - in hybrid car on display |
a pencil drawing of a zebra and her baby . |
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http://file.vintageadbrowser.com/3iodaozwajsgwb.jpg | airline -- reasons why person leads the way in experience |
http://www.remonline.com/wp-content/uploads/2017/06/Stone-South-development-1.jpg | ninety per cent of the units have balconies with views . |
https://media.gettyimages.com/photos/demonstration-of-a-group-of-people-practicing-their-rights-picture-id172184947?s=612x612 | a demonstration of a group of people practicing their rights |
http://l7.alamy.com/zooms/e163858450394e5aa21b226debd6d01f/sylvester-stallone-jennifer-flavin-and-daughters-the-expendables-2-dmrdh6.jpg | actor and daughters uk premiere held |
a fine , grainy vector pattern in black and white . |
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seamless border of orange roses and paisley , pattern on a white background . |
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http://l7.alamy.com/zooms/50937c69ef8f4289a71ef77f1ccf593f/ghana-central-region-komenda-students-in-front-of-a-school-cp4tgf.jpg | students in front of a school |
museum , opened is among the 20th - century 's most significant buildings . |
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http://l7.alamy.com/zooms/faf62ef929df42f49afde816f043a2a6/fraser-river-as-it-meets-the-pacific-ocean-where-delta-meets-richmond-erk04d.jpg | river as it meets bodies of water where airline meets |
https://media.istockphoto.com/photos/young-rock-star-jamming-on-a-guitar-picture-id185283450?k=6&m=185283450&s=612x612&w=0&h=ZRLfgSeOLNpmSH9S9uITPMGYuk7_NxoV136NdgiXoJU= | young rock star jamming on a guitar |
http://l7.alamy.com/zooms/8e24000f1ff244508864657b048b3a8b/a-moored-fishing-boat-viewed-from-bryher-isles-of-scilly-cornwall-b7g5nt.jpg | a moored fishing boat viewed from island . |
vector illustration of person isolated on a white background |
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http://www.readingchronicle.co.uk/resources/images/6179443.jpg?display=1&htype=80&type=responsive-gallery | emergency services were called after a car smashed through a set of traffic lights |
http://c8.alamy.com/comp/K32RAK/sheep-with-a-black-face-fenced-in-on-a-winter-day-K32RAK.jpg | sheep with a black face fenced in on a winter day |
http://l7.alamy.com/zooms/1ff618175b9d44a883c2a6bdf5b28fa1/a-small-park-with-american-flags-and-a-yellow-banner-around-a-tree-acfbd8.jpg | a small park with flags and a yellow banner around a tree in support of troops |
isolated water glass on a white background |
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when choosing your new outdoor color palette , opt for something that will enhance your home 's architectural style and give you plenty of curb appeal . |
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person at a corporate event |
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https://media.istockphoto.com/photos/bulldozer-on-a-building-site-picture-id467448303?k=6&m=467448303&s=612x612&w=0&h=4HNBM4KKb6ZmLpF98JWA2_eog44rbKMnfKLqR5W92hg= | bulldozer on a building site |
http://jesuitpride.com/images/2017/7/11/Fairfield_Prep_Golf_2017_10_1280x801_.jpg | person finishes at the top |
country shape animated on the satellite map of the globe |
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paths are clearly marked with signs like this . |
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putts for birdie on the second hole during the final round of the golf tournament . |
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person shot from a cliff looking out at the lake and horizon . |
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sending this angel your way ... it lights the way with it 's tiny candle . |
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i love the colours of her clothes . |
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http://www.southampton.ac.uk/~imw/jpg-Kimmeridge/9KM-kimloc-recovered-m.jpg | a very simplified location and geological map of the area and adjacent coast |
ask owner for photos of the car |
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the state of the world 's forests |
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http://oneindiaonepeople.com/wp-content/uploads/2014/02/13.jpg | the mountain slopes covered with powdery snow are popular with visitors , especially foreign tourists |
farm tractor is moving on the field , cultivating land |
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http://l7.alamy.com/zooms/6ce413683932455f9214da51ef6c8a78/master-playing-with-his-little-golden-retriever-dog-on-the-lawn-bxgte5.jpg | master playing with his little golden retriever dog on the lawn |
year later the small shrubs doubled in size . |
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according to the model , she regularly gets told it looks |
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person and the fiancee at their engagement party . |
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good night id been working like a dog i should be sleeping like a log |
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the water was never deeper than your chest . |
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http://l7.alamy.com/zooms/252ec157a42f4615a4c9639a6b1148f1/twilight-over-palazzo-dei-priori-and-the-medieval-town-of-volterra-hfpkxa.jpg | twilight over italian gothic structure and the medieval town |
a simple wedding cake with lego bride and groom topper and cake pops . |
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simple custom leather pulls for your dresser , cabinets , or doors . |
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http://www.hollowtop.com/journals/Tongue_River_Photos/Sunny_Morning.jpg | sunny woodland morning along river . |
person , why u turn me on with your designs ? i believe i will bepinning some more of his work here in a bit . |
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http://media.gettyimages.com/photos/entertainer-beyonce-performs-on-stage-during-the-formation-world-tour-picture-id538724332 | pop artist performs on stage . |
https://media.istockphoto.com/photos/industrial-plants-in-the-distance-at-night-picture-id479157539?k=6&m=479157539&s=612x612&w=0&h=0EmjeUArhPcd38poc3cqx_bq0-IKvnfh1qhlxCfesM4= | industrial plants in the distance at night |
http://l7.alamy.com/zooms/476f507779b646c4b9ae7e3894c76eb8/the-empire-state-building-and-one-world-trade-center-light-up-at-night-fcn345.jpg | building and skyscraper light up at night |
actor is an actor who started out as a lawyer . |
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color good with green couch in living room ... favorite ... i think maybe a little too peach ? |
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http://www.fourintravels.com/wp-content/uploads/2013/12/The-villas-from-the-front1.jpg | the villas from the front |
tree branches swing at real time with the wind while clouds move fast in time lapse |
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https://burpple-1.imgix.net/foods/2cb7a04812e4af46eb01248319_original.?w=645&dpr=1&fit=crop&q=80 | my coffee of the day ! |
Dataset Card for Conceptual Captions
Dataset Summary
Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the current version of the captions, we have developed an automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions.
Dataset Preprocessing
This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
USER_AGENT = get_datasets_user_agent()
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": USER_AGENT},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
return batch
num_threads = 20
dset = load_dataset("google-research-datasets/conceptual_captions")
dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
Supported Tasks and Leaderboards
image-captioning
: This dataset can be used to train model for the Image Captioning task. The leaderboard for this task is available here. Official submission output captions are scored against the reference captions from the hidden test set using this implementation of the CIDEr (primary), ROUGE-L and SPICE metrics.
Languages
All captions are in English.
Dataset Structure
Data Instances
unlabeled
Each instance in this configuration represents a single image with a caption:
{
'image_url': 'http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800',
'caption': 'a very typical bus station'
}
labeled
Each instance in this configuration represents a single image with a caption with addtional machine-generated image labels and confidence scores:
{
'image_url': 'https://thumb1.shutterstock.com/display_pic_with_logo/261388/223876810/stock-vector-christmas-tree-on-a-black-background-vector-223876810.jpg',
'caption': 'christmas tree on a black background .',
'labels': ['christmas tree', 'christmas decoration', 'font', 'text', 'graphic design', 'illustration','interior design', 'tree', 'christmas eve', 'ornament', 'fir', 'plant', 'pine', 'pine family', 'graphics'],
'MIDs': ['/m/025nd', '/m/05fc9mj', '/m/03gq5hm', '/m/07s6nbt', '/m/03c31', '/m/01kr8f', '/m/0h8nzzj', '/m/07j7r', '/m/014r1s', '/m/05ykl4', '/m/016x4z', '/m/05s2s', '/m/09t57', '/m/01tfm0', '/m/021sdg'],
'confidence_scores': [0.9818305373191833, 0.952756941318512, 0.9227379560470581, 0.8524878621101379, 0.7597672343254089, 0.7493422031402588, 0.7332468628883362, 0.6869218349456787, 0.6552258133888245, 0.6357356309890747, 0.5992692708969116, 0.585474967956543, 0.5222904086112976, 0.5113164782524109, 0.5036579966545105]
}
Data Fields
unlabeled
image_url
: Static URL for downloading the image associated with the post.caption
: Textual description of the image.
labeled
image_url
: Static URL for downloading the image associated with the post.caption
: Textual description of the image.labels
: A sequence of machine-generated labels obtained using the Google Cloud Vision API.MIDs
: A sequence of machine-generated identifiers (MID) corresponding to the label's Google Knowledge Graph entry.confidence_scores
: A sequence of confidence scores denoting how likely the corresponing labels are present on the image.
Data Splits
unlabeled
The basic version of the dataset split into Training and Validation splits. The Training split consists of 3,318,333 image-URL/caption pairs and the Validation split consists of 15,840 image-URL/caption pairs.
labeled
The labeled version of the dataset with a single. The entire data is contained in Training split, which is a subset of 2,007,090 image-URL/caption pairs from the Training set of the unlabeled
config.
Dataset Creation
Curation Rationale
From the paper:
In this paper, we make contributions to both the data and modeling categories. First, we present a new dataset of caption annotations Conceptual Captions (Fig. 1), which has an order of magnitude more images than the COCO dataset. Conceptual Captions consists of about 3.3M himage, descriptioni pairs. In contrast with the curated style of the COCO images, Conceptual Captions images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles.
Source Data
Initial Data Collection and Normalization
From the homepage:
For Conceptual Captions, we developed a fully automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions. Because no human annotators are involved, the Conceptual Captions dataset generation process is highly scalable.
To generate this dataset, we started with a Flume pipeline that processes billions of Internet webpages, extracting, filtering, and processing candidate image and caption pairs, and keeping those that pass through several filters.
We first screen for certain properties like size, aspect ratio, adult content scores. These filters discard more than 65% of the candidates. Next, we use Alt-Texts for text-based filtering, removing captions with non-descriptive text (such as SEO tags or hashtags); we also discard texts with high sentiment polarity or adult content scores, resulting in just 3% of the incoming candidates passing through.
In the next step, we filter out candidates for which none of the text tokens can be mapped to the visual content of the image. We use image classifiers (e.g., Google Cloud Vision APIs) to assign class labels to images and match these labels against the candidate text (allowing morphological transformations), discarding >around 60% of the candidates that reach this stage.
The candidates passing the above filters tend to be good Alt-text image descriptions. However, a large majority of these use proper names (for people, venues, locations, etc.), brands, dates, quotes, etc. This creates two distinct problems. First, some of these cannot be inferred based on the image pixels alone. This is problematic because unless the image has the necessary visual information it is not useful for training. Second, even if the proper names could be inferred from the image it is extremely difficult for a model to learn to perform both fine-grained classification and natural-language descriptions simultaneously. We posit that if automatic determination of names, locations, brands, etc. is needed, it should be done as a separate task that may leverage image meta-information (e.g. GPS info), or complementary techniques such as OCR.
We address the above problems with the insight that proper names should be replaced by words that represent the same general notion, i.e., by their concept. For example, we remove locations (“Crowd at a concert in Los Angeles“ becomes “Crowd at a concert”), names (e.g., “Former Miss World Priyanka Chopra on the red carpet” becomes “actor on the red carpet”), proper noun modifiers (e.g., “Italian cuisine” becomes just “cuisine”) and noun phrases (e.g., “actor and actor” becomes “actors”). Around 20% of the samples are discarded during this transformation because it can leave sentences too short, or otherwise inconsistent.
Finally, we perform another round of filtering to identify concepts with low-count. We cluster all resolved entities (e.g., “actor”, “dog”, “neighborhood”, etc.) and keep only the candidate types which have a count of over 100 mentions. This retains around 16K entity concepts such as: “person”, “actor”, “artist”, “player” and “illustration”. The less frequent ones that we dropped include “baguette”, “bridle”, “deadline”, “ministry” and “funnel”.
Who are the source language producers?
Not specified.
Annotations
Annotation process
Annotations are extracted jointly with the images using the automatic pipeline.
Who are the annotators?
Not specified.
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
Piyush Sharma, Nan Ding, Sebastian Goodman and Radu Soricut.
Licensing Information
The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
Citation Information
@inproceedings{sharma2018conceptual,
title = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning},
author = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu},
booktitle = {Proceedings of ACL},
year = {2018},
}
Contributions
Thanks to @abhishekkrthakur and @mariosasko for adding this dataset.
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Models trained or fine-tuned on google-research-datasets/conceptual_captions
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