Datasets:
metadata
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': air_freshener
'1': alarm_clock
'2': backpack
'3': baking_sheet
'4': banana
'5': band_aid
'6': baseball_bat
'7': baseball_glove
'8': basket
'9': bathrobe
'10': battery
'11': bed_sheet
'12': beer_bottle
'13': beer_can
'14': belt
'15': bench
'16': bicycle
'17': bike_pump
'18': bills_money
'19': binder_closed
'20': biscuits
'21': blanket
'22': blender
'23': blouse
'24': board_game
'25': book_closed
'26': bookend
'27': boots
'28': bottle_cap
'29': bottle_opener
'30': bottle_stopper
'31': box
'32': bracelet
'33': bread_knife
'34': bread_loaf
'35': briefcase
'36': brooch
'37': broom
'38': bucket
'39': butchers_knife
'40': butter
'41': button
'42': calendar
'43': can_opener
'44': candle
'45': canned_food
'46': cd_case
'47': cellphone
'48': cellphone_case
'49': cellphone_charger
'50': cereal
'51': chair
'52': cheese
'53': chess_piece
'54': chocolate
'55': chopstick
'56': clothes_hamper
'57': clothes_hanger
'58': coaster
'59': coffee_beans
'60': coffee_french_press
'61': coffee_grinder
'62': coffee_machine
'63': coffee_table
'64': coin_money
'65': comb
'66': combination_lock
'67': computer_mouse
'68': contact_lens_case
'69': cooking_oil_bottle
'70': cork
'71': cutting_board
'72': deodorant
'73': desk_lamp
'74': detergent
'75': dish_soap
'76': document_folder_closed
'77': dog_bed
'78': doormat
'79': drawer_open
'80': dress
'81': dress_pants
'82': dress_shirt
'83': dress_shoe_men
'84': dress_shoe_women
'85': drill
'86': drinking_cup
'87': drinking_straw
'88': drying_rack_for_clothes
'89': drying_rack_for_dishes
'90': dust_pan
'91': dvd_player
'92': earbuds
'93': earring
'94': egg
'95': egg_carton
'96': envelope
'97': eraser_white_board
'98': extension_cable
'99': eyeglasses
'100': fan
'101': figurine_or_statue
'102': first_aid_kit
'103': flashlight
'104': floss_container
'105': flour_container
'106': fork
'107': frying_pan
'108': full_sized_towel
'109': glue_container
'110': hair_brush
'111': hair_dryer
'112': hairclip
'113': hairtie
'114': hammer
'115': hand_mirror
'116': hand_towel_or_rag
'117': handbag
'118': hat
'119': headphones_over_ear
'120': helmet
'121': honey_container
'122': ice
'123': ice_cube_tray
'124': iron_for_clothes
'125': ironing_board
'126': jam
'127': jar
'128': jeans
'129': kettle
'130': key_chain
'131': keyboard
'132': ladle
'133': lampshade
'134': laptop_charger
'135': laptop_open
'136': leaf
'137': leggings
'138': lemon
'139': letter_opener
'140': lettuce
'141': light_bulb
'142': lighter
'143': lipstick
'144': loofah
'145': magazine
'146': makeup
'147': makeup_brush
'148': marker
'149': match
'150': measuring_cup
'151': microwave
'152': milk
'153': mixing_salad_bowl
'154': monitor
'155': mouse_pad
'156': mouthwash
'157': mug
'158': multitool
'159': nail_clippers
'160': nail_fastener
'161': nail_file
'162': nail_polish
'163': napkin
'164': necklace
'165': newspaper
'166': night_light
'167': nightstand
'168': notebook
'169': notepad
'170': nut_for_screw
'171': orange
'172': oven_mitts
'173': padlock
'174': paint_can
'175': paintbrush
'176': paper
'177': paper_bag
'178': paper_plates
'179': paper_towel
'180': paperclip
'181': peeler
'182': pen
'183': pencil
'184': pepper_shaker
'185': pet_food_container
'186': phone_landline
'187': photograph_printed
'188': pill_bottle
'189': pill_organizer
'190': pillow
'191': pitcher
'192': placemat
'193': plastic_bag
'194': plastic_cup
'195': plastic_wrap
'196': plate
'197': playing_cards
'198': pliers
'199': plunger
'200': pop_can
'201': portable_heater
'202': poster
'203': power_bar
'204': power_cable
'205': printer
'206': raincoat
'207': rake
'208': razor
'209': receipt
'210': remote_control
'211': removable_blade
'212': ribbon
'213': ring
'214': rock
'215': rolling_pin
'216': ruler
'217': running_shoe
'218': safety_pin
'219': salt_shaker
'220': sandal
'221': scarf
'222': scissors
'223': screw
'224': scrub_brush
'225': sewing_kit
'226': shampoo_bottle
'227': shoelace
'228': shorts
'229': shovel
'230': skateboard
'231': skirt
'232': sleeping_bag
'233': slipper
'234': soap_bar
'235': soap_dispenser
'236': sock
'237': soup_bowl
'238': spatula
'239': speaker
'240': sponge
'241': spoon
'242': spray_bottle
'243': squeegee
'244': squeeze_bottle
'245': standing_lamp
'246': stapler
'247': step_stool
'248': still_camera
'249': stopper_sink_tub
'250': strainer
'251': stuffed_animal
'252': sugar_container
'253': suit_jacket
'254': suitcase
'255': sunglasses
'256': sweater
'257': swimming_trunks
'258': t-shirt
'259': table_knife
'260': tablecloth
'261': tablet_ipad
'262': tanktop
'263': tape
'264': tape_measure
'265': tarp
'266': teabag
'267': teapot
'268': tennis_racket
'269': thermometer
'270': thermos
'271': throw_pillow
'272': tie
'273': tissue
'274': toaster
'275': toilet_paper_roll
'276': tomato
'277': tongs
'278': toothbrush
'279': toothpaste
'280': tote_bag
'281': toy
'282': trash_bag
'283': trash_bin
'284': travel_case
'285': tray
'286': trophy
'287': tv
'288': tweezers
'289': umbrella
'290': usb_cable
'291': usb_flash_drive
'292': vacuum_cleaner
'293': vase
'294': video_camera
'295': walker
'296': walking_cane
'297': wallet
'298': watch
'299': water_bottle
'300': water_filter
'301': webcam
'302': weight_exercise
'303': weight_scale
'304': wheel
'305': whisk
'306': whistle
'307': wine_bottle
'308': wine_glass
'309': winter_glove
'310': wok
'311': wrench
'312': ziploc_bag
- name: imagenet_labels
sequence: int64
- name: imagenet_synsets
sequence: string
splits:
- name: test
num_bytes: 127647283245.571
num_examples: 50273
download_size: 125292547404
dataset_size: 127647283245.571
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
license: other
task_categories:
- image-classification
pretty_name: ObjectNet
size_categories:
- 10K<n<100K
extra_gated_prompt: >-
By clicking on “Access repository” below, you also agree to ObjectNet Terms:
ObjectNet is free to use for both research and commercial applications. The
authors own the source images and allow their use under a license derived from
Creative Commons Attribution 4.0 with only two additional clauses.
1. ObjectNet may never be used to tune the parameters of any model.
2. Any individual images from ObjectNet may only be posted to the web
including their 1 pixel red border.
If you are using ObjectNet, please cite our work, the citation appears at the
bottom of this page. Any derivative of ObjectNet must contain attribution as
well.
ObjectNet
A webp (lossless) encoded version of ObjectNet-1.0 at original resolution.
License / Usage Terms
ObjectNet is free to use for both research and commercial applications. The authors own the source images and allow their use under a license derived from Creative Commons Attribution 4.0 with only two additional clauses.
- ObjectNet may never be used to tune the parameters of any model.
- Any individual images from ObjectNet may only be posted to the web including their 1 pixel red border. If you are using ObjectNet, please cite our work, the citation appears at the bottom of this page. Any derivative of ObjectNet must contain attribution as well.
About
What is ObjectNet?
- A new kind of vision dataset borrowing the idea of controls from other areas of science.
- No training set, only a test set! Put your vision system through its paces.
- Collected to intentionally show objects from new viewpoints on new backgrounds.
- 50,000 image test set, same as ImageNet, with controls for rotation, background, and viewpoint.
- 313 object classes with 113 overlapping ImageNet
- Large performance drop, what you can expect from vision systems in the real world!
- Robust to fine-tuning and a very difficult transfer learning problem
Why the Red Borders / How do I recognize if an image is in ObjectNet?
As training sets become huge, the risk that test and training sets overlap is serious. We provide ObjectNet with a 2 pixel red border around each image which must be removed before performing inference. The ObjectNet license requires that if you post images from ObjectNet to the web, you include this border. Any time you see an image with a solid 2 pixel red border, that's an indication it's in someone's test set and you should be careful about training on it. Reverse image search will allow you to figure out which test set it is from.
NOTE: original ObjectNet PNG files actually have a 2 pixel red border while their descriptions say 1.
Preprocessing Steps for This timm Version
- Re-encode PNG images with lossless WebP (~32% reduction in size), keeping red border.
- Add
imagenet_labelsandimagenet_synsetsconsisting of lists of ImageNet-1k classes that overlap with ObjectNet class.
Citation
@incollection{NIPS2019_9142,
title = {ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models},
author = {Barbu, Andrei and Mayo, David and Alverio, Julian and Luo, William and Wang, Christopher and Gutfreund, Dan and Tenenbaum, Josh and Katz, Boris},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {9448--9458},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf}
}