Datasets:
Tasks:
Image Segmentation
Sub-tasks:
instance-segmentation
Languages:
English
Size:
10K<n<100K
ArXiv:
Tags:
scene-parsing
License:
File size: 47,918 Bytes
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---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- bsd-3-clause
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|ade20k
task_categories:
- image-segmentation
task_ids:
- instance-segmentation
paperswithcode_id: ade20k
pretty_name: MIT Scene Parsing Benchmark
tags:
- scene-parsing
dataset_info:
- config_name: scene_parsing
features:
- name: image
dtype: image
- name: annotation
dtype: image
- name: scene_category
dtype:
class_label:
names:
0: airport_terminal
1: art_gallery
2: badlands
3: ball_pit
4: bathroom
5: beach
6: bedroom
7: booth_indoor
8: botanical_garden
9: bridge
10: bullring
11: bus_interior
12: butte
13: canyon
14: casino_outdoor
15: castle
16: church_outdoor
17: closet
18: coast
19: conference_room
20: construction_site
21: corral
22: corridor
23: crosswalk
24: day_care_center
25: sand
26: elevator_interior
27: escalator_indoor
28: forest_road
29: gangplank
30: gas_station
31: golf_course
32: gymnasium_indoor
33: harbor
34: hayfield
35: heath
36: hoodoo
37: house
38: hunting_lodge_outdoor
39: ice_shelf
40: joss_house
41: kiosk_indoor
42: kitchen
43: landfill
44: library_indoor
45: lido_deck_outdoor
46: living_room
47: locker_room
48: market_outdoor
49: mountain_snowy
50: office
51: orchard
52: arbor
53: bookshelf
54: mews
55: nook
56: preserve
57: traffic_island
58: palace
59: palace_hall
60: pantry
61: patio
62: phone_booth
63: establishment
64: poolroom_home
65: quonset_hut_outdoor
66: rice_paddy
67: sandbox
68: shopfront
69: skyscraper
70: stone_circle
71: subway_interior
72: platform
73: supermarket
74: swimming_pool_outdoor
75: television_studio
76: indoor_procenium
77: train_railway
78: coral_reef
79: viaduct
80: wave
81: wind_farm
82: bottle_storage
83: abbey
84: access_road
85: air_base
86: airfield
87: airlock
88: airplane_cabin
89: airport
90: entrance
91: airport_ticket_counter
92: alcove
93: alley
94: amphitheater
95: amusement_arcade
96: amusement_park
97: anechoic_chamber
98: apartment_building_outdoor
99: apse_indoor
100: apse_outdoor
101: aquarium
102: aquatic_theater
103: aqueduct
104: arcade
105: arch
106: archaelogical_excavation
107: archive
108: basketball
109: football
110: hockey
111: performance
112: rodeo
113: soccer
114: armory
115: army_base
116: arrival_gate_indoor
117: arrival_gate_outdoor
118: art_school
119: art_studio
120: artists_loft
121: assembly_line
122: athletic_field_indoor
123: athletic_field_outdoor
124: atrium_home
125: atrium_public
126: attic
127: auditorium
128: auto_factory
129: auto_mechanics_indoor
130: auto_mechanics_outdoor
131: auto_racing_paddock
132: auto_showroom
133: backstage
134: backstairs
135: badminton_court_indoor
136: badminton_court_outdoor
137: baggage_claim
138: shop
139: exterior
140: balcony_interior
141: ballroom
142: bamboo_forest
143: bank_indoor
144: bank_outdoor
145: bank_vault
146: banquet_hall
147: baptistry_indoor
148: baptistry_outdoor
149: bar
150: barbershop
151: barn
152: barndoor
153: barnyard
154: barrack
155: baseball_field
156: basement
157: basilica
158: basketball_court_indoor
159: basketball_court_outdoor
160: bathhouse
161: batters_box
162: batting_cage_indoor
163: batting_cage_outdoor
164: battlement
165: bayou
166: bazaar_indoor
167: bazaar_outdoor
168: beach_house
169: beauty_salon
170: bedchamber
171: beer_garden
172: beer_hall
173: belfry
174: bell_foundry
175: berth
176: berth_deck
177: betting_shop
178: bicycle_racks
179: bindery
180: biology_laboratory
181: bistro_indoor
182: bistro_outdoor
183: bleachers_indoor
184: bleachers_outdoor
185: boardwalk
186: boat_deck
187: boathouse
188: bog
189: bomb_shelter_indoor
190: bookbindery
191: bookstore
192: bow_window_indoor
193: bow_window_outdoor
194: bowling_alley
195: box_seat
196: boxing_ring
197: breakroom
198: brewery_indoor
199: brewery_outdoor
200: brickyard_indoor
201: brickyard_outdoor
202: building_complex
203: building_facade
204: bullpen
205: burial_chamber
206: bus_depot_indoor
207: bus_depot_outdoor
208: bus_shelter
209: bus_station_indoor
210: bus_station_outdoor
211: butchers_shop
212: cabana
213: cabin_indoor
214: cabin_outdoor
215: cafeteria
216: call_center
217: campsite
218: campus
219: natural
220: urban
221: candy_store
222: canteen
223: car_dealership
224: backseat
225: frontseat
226: caravansary
227: cardroom
228: cargo_container_interior
229: airplane
230: boat
231: freestanding
232: carport_indoor
233: carport_outdoor
234: carrousel
235: casino_indoor
236: catacomb
237: cathedral_indoor
238: cathedral_outdoor
239: catwalk
240: cavern_indoor
241: cavern_outdoor
242: cemetery
243: chalet
244: chaparral
245: chapel
246: checkout_counter
247: cheese_factory
248: chemical_plant
249: chemistry_lab
250: chicken_coop_indoor
251: chicken_coop_outdoor
252: chicken_farm_indoor
253: chicken_farm_outdoor
254: childs_room
255: choir_loft_interior
256: church_indoor
257: circus_tent_indoor
258: circus_tent_outdoor
259: city
260: classroom
261: clean_room
262: cliff
263: booth
264: room
265: clock_tower_indoor
266: cloister_indoor
267: cloister_outdoor
268: clothing_store
269: coast_road
270: cockpit
271: coffee_shop
272: computer_room
273: conference_center
274: conference_hall
275: confessional
276: control_room
277: control_tower_indoor
278: control_tower_outdoor
279: convenience_store_indoor
280: convenience_store_outdoor
281: corn_field
282: cottage
283: cottage_garden
284: courthouse
285: courtroom
286: courtyard
287: covered_bridge_interior
288: crawl_space
289: creek
290: crevasse
291: library
292: cybercafe
293: dacha
294: dairy_indoor
295: dairy_outdoor
296: dam
297: dance_school
298: darkroom
299: delicatessen
300: dentists_office
301: department_store
302: departure_lounge
303: vegetation
304: desert_road
305: diner_indoor
306: diner_outdoor
307: dinette_home
308: vehicle
309: dining_car
310: dining_hall
311: dining_room
312: dirt_track
313: discotheque
314: distillery
315: ditch
316: dock
317: dolmen
318: donjon
319: doorway_indoor
320: doorway_outdoor
321: dorm_room
322: downtown
323: drainage_ditch
324: dress_shop
325: dressing_room
326: drill_rig
327: driveway
328: driving_range_indoor
329: driving_range_outdoor
330: drugstore
331: dry_dock
332: dugout
333: earth_fissure
334: editing_room
335: electrical_substation
336: elevated_catwalk
337: door
338: freight_elevator
339: elevator_lobby
340: elevator_shaft
341: embankment
342: embassy
343: engine_room
344: entrance_hall
345: escalator_outdoor
346: escarpment
347: estuary
348: excavation
349: exhibition_hall
350: fabric_store
351: factory_indoor
352: factory_outdoor
353: fairway
354: farm
355: fastfood_restaurant
356: fence
357: cargo_deck
358: ferryboat_indoor
359: passenger_deck
360: cultivated
361: wild
362: field_road
363: fire_escape
364: fire_station
365: firing_range_indoor
366: firing_range_outdoor
367: fish_farm
368: fishmarket
369: fishpond
370: fitting_room_interior
371: fjord
372: flea_market_indoor
373: flea_market_outdoor
374: floating_dry_dock
375: flood
376: florist_shop_indoor
377: florist_shop_outdoor
378: fly_bridge
379: food_court
380: football_field
381: broadleaf
382: needleleaf
383: forest_fire
384: forest_path
385: formal_garden
386: fort
387: fortress
388: foundry_indoor
389: foundry_outdoor
390: fountain
391: freeway
392: funeral_chapel
393: funeral_home
394: furnace_room
395: galley
396: game_room
397: garage_indoor
398: garage_outdoor
399: garbage_dump
400: gasworks
401: gate
402: gatehouse
403: gazebo_interior
404: general_store_indoor
405: general_store_outdoor
406: geodesic_dome_indoor
407: geodesic_dome_outdoor
408: ghost_town
409: gift_shop
410: glacier
411: glade
412: gorge
413: granary
414: great_hall
415: greengrocery
416: greenhouse_indoor
417: greenhouse_outdoor
418: grotto
419: guardhouse
420: gulch
421: gun_deck_indoor
422: gun_deck_outdoor
423: gun_store
424: hacienda
425: hallway
426: handball_court
427: hangar_indoor
428: hangar_outdoor
429: hardware_store
430: hat_shop
431: hatchery
432: hayloft
433: hearth
434: hedge_maze
435: hedgerow
436: heliport
437: herb_garden
438: highway
439: hill
440: home_office
441: home_theater
442: hospital
443: hospital_room
444: hot_spring
445: hot_tub_indoor
446: hot_tub_outdoor
447: hotel_outdoor
448: hotel_breakfast_area
449: hotel_room
450: hunting_lodge_indoor
451: hut
452: ice_cream_parlor
453: ice_floe
454: ice_skating_rink_indoor
455: ice_skating_rink_outdoor
456: iceberg
457: igloo
458: imaret
459: incinerator_indoor
460: incinerator_outdoor
461: industrial_area
462: industrial_park
463: inn_indoor
464: inn_outdoor
465: irrigation_ditch
466: islet
467: jacuzzi_indoor
468: jacuzzi_outdoor
469: jail_indoor
470: jail_outdoor
471: jail_cell
472: japanese_garden
473: jetty
474: jewelry_shop
475: junk_pile
476: junkyard
477: jury_box
478: kasbah
479: kennel_indoor
480: kennel_outdoor
481: kindergarden_classroom
482: kiosk_outdoor
483: kitchenette
484: lab_classroom
485: labyrinth_indoor
486: labyrinth_outdoor
487: lagoon
488: artificial
489: landing
490: landing_deck
491: laundromat
492: lava_flow
493: lavatory
494: lawn
495: lean-to
496: lecture_room
497: legislative_chamber
498: levee
499: library_outdoor
500: lido_deck_indoor
501: lift_bridge
502: lighthouse
503: limousine_interior
504: liquor_store_indoor
505: liquor_store_outdoor
506: loading_dock
507: lobby
508: lock_chamber
509: loft
510: lookout_station_indoor
511: lookout_station_outdoor
512: lumberyard_indoor
513: lumberyard_outdoor
514: machine_shop
515: manhole
516: mansion
517: manufactured_home
518: market_indoor
519: marsh
520: martial_arts_gym
521: mastaba
522: maternity_ward
523: mausoleum
524: medina
525: menhir
526: mesa
527: mess_hall
528: mezzanine
529: military_hospital
530: military_hut
531: military_tent
532: mine
533: mineshaft
534: mini_golf_course_indoor
535: mini_golf_course_outdoor
536: mission
537: dry
538: water
539: mobile_home
540: monastery_indoor
541: monastery_outdoor
542: moon_bounce
543: moor
544: morgue
545: mosque_indoor
546: mosque_outdoor
547: motel
548: mountain
549: mountain_path
550: mountain_road
551: movie_theater_indoor
552: movie_theater_outdoor
553: mudflat
554: museum_indoor
555: museum_outdoor
556: music_store
557: music_studio
558: misc
559: natural_history_museum
560: naval_base
561: newsroom
562: newsstand_indoor
563: newsstand_outdoor
564: nightclub
565: nuclear_power_plant_indoor
566: nuclear_power_plant_outdoor
567: nunnery
568: nursery
569: nursing_home
570: oasis
571: oast_house
572: observatory_indoor
573: observatory_outdoor
574: observatory_post
575: ocean
576: office_building
577: office_cubicles
578: oil_refinery_indoor
579: oil_refinery_outdoor
580: oilrig
581: operating_room
582: optician
583: organ_loft_interior
584: orlop_deck
585: ossuary
586: outcropping
587: outhouse_indoor
588: outhouse_outdoor
589: overpass
590: oyster_bar
591: oyster_farm
592: acropolis
593: aircraft_carrier_object
594: amphitheater_indoor
595: archipelago
596: questionable
597: assembly_hall
598: assembly_plant
599: awning_deck
600: back_porch
601: backdrop
602: backroom
603: backstage_outdoor
604: backstairs_indoor
605: backwoods
606: ballet
607: balustrade
608: barbeque
609: basin_outdoor
610: bath_indoor
611: bath_outdoor
612: bathhouse_outdoor
613: battlefield
614: bay
615: booth_outdoor
616: bottomland
617: breakfast_table
618: bric-a-brac
619: brooklet
620: bubble_chamber
621: buffet
622: bulkhead
623: bunk_bed
624: bypass
625: byroad
626: cabin_cruiser
627: cargo_helicopter
628: cellar
629: chair_lift
630: cocktail_lounge
631: corner
632: country_house
633: country_road
634: customhouse
635: dance_floor
636: deck-house_boat_deck_house
637: deck-house_deck_house
638: dining_area
639: diving_board
640: embrasure
641: entranceway_indoor
642: entranceway_outdoor
643: entryway_outdoor
644: estaminet
645: farm_building
646: farmhouse
647: feed_bunk
648: field_house
649: field_tent_indoor
650: field_tent_outdoor
651: fire_trench
652: fireplace
653: flashflood
654: flatlet
655: floating_dock
656: flood_plain
657: flowerbed
658: flume_indoor
659: flying_buttress
660: foothill
661: forecourt
662: foreshore
663: front_porch
664: garden
665: gas_well
666: glen
667: grape_arbor
668: grove
669: guardroom
670: guesthouse
671: gymnasium_outdoor
672: head_shop
673: hen_yard
674: hillock
675: housing_estate
676: housing_project
677: howdah
678: inlet
679: insane_asylum
680: outside
681: juke_joint
682: jungle
683: kraal
684: laboratorywet
685: landing_strip
686: layby
687: lean-to_tent
688: loge
689: loggia_outdoor
690: lower_deck
691: luggage_van
692: mansard
693: meadow
694: meat_house
695: megalith
696: mens_store_outdoor
697: mental_institution_indoor
698: mental_institution_outdoor
699: military_headquarters
700: millpond
701: millrace
702: natural_spring
703: nursing_home_outdoor
704: observation_station
705: open-hearth_furnace
706: operating_table
707: outbuilding
708: palestra
709: parkway
710: patio_indoor
711: pavement
712: pawnshop_outdoor
713: pinetum
714: piste_road
715: pizzeria_outdoor
716: powder_room
717: pumping_station
718: reception_room
719: rest_stop
720: retaining_wall
721: rift_valley
722: road
723: rock_garden
724: rotisserie
725: safari_park
726: salon
727: saloon
728: sanatorium
729: science_laboratory
730: scrubland
731: scullery
732: seaside
733: semidesert
734: shelter
735: shelter_deck
736: shelter_tent
737: shore
738: shrubbery
739: sidewalk
740: snack_bar
741: snowbank
742: stage_set
743: stall
744: stateroom
745: store
746: streetcar_track
747: student_center
748: study_hall
749: sugar_refinery
750: sunroom
751: supply_chamber
752: t-bar_lift
753: tannery
754: teahouse
755: threshing_floor
756: ticket_window_indoor
757: tidal_basin
758: tidal_river
759: tiltyard
760: tollgate
761: tomb
762: tract_housing
763: trellis
764: truck_stop
765: upper_balcony
766: vestibule
767: vinery
768: walkway
769: war_room
770: washroom
771: water_fountain
772: water_gate
773: waterscape
774: waterway
775: wetland
776: widows_walk_indoor
777: windstorm
778: packaging_plant
779: pagoda
780: paper_mill
781: park
782: parking_garage_indoor
783: parking_garage_outdoor
784: parking_lot
785: parlor
786: particle_accelerator
787: party_tent_indoor
788: party_tent_outdoor
789: pasture
790: pavilion
791: pawnshop
792: pedestrian_overpass_indoor
793: penalty_box
794: pet_shop
795: pharmacy
796: physics_laboratory
797: piano_store
798: picnic_area
799: pier
800: pig_farm
801: pilothouse_indoor
802: pilothouse_outdoor
803: pitchers_mound
804: pizzeria
805: planetarium_indoor
806: planetarium_outdoor
807: plantation_house
808: playground
809: playroom
810: plaza
811: podium_indoor
812: podium_outdoor
813: police_station
814: pond
815: pontoon_bridge
816: poop_deck
817: porch
818: portico
819: portrait_studio
820: postern
821: power_plant_outdoor
822: print_shop
823: priory
824: promenade
825: promenade_deck
826: pub_indoor
827: pub_outdoor
828: pulpit
829: putting_green
830: quadrangle
831: quicksand
832: quonset_hut_indoor
833: racecourse
834: raceway
835: raft
836: railroad_track
837: railway_yard
838: rainforest
839: ramp
840: ranch
841: ranch_house
842: reading_room
843: reception
844: recreation_room
845: rectory
846: recycling_plant_indoor
847: refectory
848: repair_shop
849: residential_neighborhood
850: resort
851: rest_area
852: restaurant
853: restaurant_kitchen
854: restaurant_patio
855: restroom_indoor
856: restroom_outdoor
857: revolving_door
858: riding_arena
859: river
860: road_cut
861: rock_arch
862: roller_skating_rink_indoor
863: roller_skating_rink_outdoor
864: rolling_mill
865: roof
866: roof_garden
867: root_cellar
868: rope_bridge
869: roundabout
870: roundhouse
871: rubble
872: ruin
873: runway
874: sacristy
875: salt_plain
876: sand_trap
877: sandbar
878: sauna
879: savanna
880: sawmill
881: schoolhouse
882: schoolyard
883: science_museum
884: scriptorium
885: sea_cliff
886: seawall
887: security_check_point
888: server_room
889: sewer
890: sewing_room
891: shed
892: shipping_room
893: shipyard_outdoor
894: shoe_shop
895: shopping_mall_indoor
896: shopping_mall_outdoor
897: shower
898: shower_room
899: shrine
900: signal_box
901: sinkhole
902: ski_jump
903: ski_lodge
904: ski_resort
905: ski_slope
906: sky
907: skywalk_indoor
908: skywalk_outdoor
909: slum
910: snowfield
911: massage_room
912: mineral_bath
913: spillway
914: sporting_goods_store
915: squash_court
916: stable
917: baseball
918: stadium_outdoor
919: stage_indoor
920: stage_outdoor
921: staircase
922: starting_gate
923: steam_plant_outdoor
924: steel_mill_indoor
925: storage_room
926: storm_cellar
927: street
928: strip_mall
929: strip_mine
930: student_residence
931: submarine_interior
932: sun_deck
933: sushi_bar
934: swamp
935: swimming_hole
936: swimming_pool_indoor
937: synagogue_indoor
938: synagogue_outdoor
939: taxistand
940: taxiway
941: tea_garden
942: tearoom
943: teashop
944: television_room
945: east_asia
946: mesoamerican
947: south_asia
948: western
949: tennis_court_indoor
950: tennis_court_outdoor
951: tent_outdoor
952: terrace_farm
953: indoor_round
954: indoor_seats
955: theater_outdoor
956: thriftshop
957: throne_room
958: ticket_booth
959: tobacco_shop_indoor
960: toll_plaza
961: tollbooth
962: topiary_garden
963: tower
964: town_house
965: toyshop
966: track_outdoor
967: trading_floor
968: trailer_park
969: train_interior
970: train_station_outdoor
971: station
972: tree_farm
973: tree_house
974: trench
975: trestle_bridge
976: tundra
977: rail_indoor
978: rail_outdoor
979: road_indoor
980: road_outdoor
981: turkish_bath
982: ocean_deep
983: ocean_shallow
984: utility_room
985: valley
986: van_interior
987: vegetable_garden
988: velodrome_indoor
989: velodrome_outdoor
990: ventilation_shaft
991: veranda
992: vestry
993: veterinarians_office
994: videostore
995: village
996: vineyard
997: volcano
998: volleyball_court_indoor
999: volleyball_court_outdoor
1000: voting_booth
1001: waiting_room
1002: walk_in_freezer
1003: warehouse_indoor
1004: warehouse_outdoor
1005: washhouse_indoor
1006: washhouse_outdoor
1007: watchtower
1008: water_mill
1009: water_park
1010: water_tower
1011: water_treatment_plant_indoor
1012: water_treatment_plant_outdoor
1013: block
1014: cascade
1015: cataract
1016: fan
1017: plunge
1018: watering_hole
1019: weighbridge
1020: wet_bar
1021: wharf
1022: wheat_field
1023: whispering_gallery
1024: widows_walk_interior
1025: windmill
1026: window_seat
1027: barrel_storage
1028: winery
1029: witness_stand
1030: woodland
1031: workroom
1032: workshop
1033: wrestling_ring_indoor
1034: wrestling_ring_outdoor
1035: yard
1036: youth_hostel
1037: zen_garden
1038: ziggurat
1039: zoo
1040: forklift
1041: hollow
1042: hutment
1043: pueblo
1044: vat
1045: perfume_shop
1046: steel_mill_outdoor
1047: orchestra_pit
1048: bridle_path
1049: lyceum
1050: one-way_street
1051: parade_ground
1052: pump_room
1053: recycling_plant_outdoor
1054: chuck_wagon
splits:
- name: train
num_bytes: 8468086
num_examples: 20210
- name: test
num_bytes: 744607
num_examples: 3352
- name: validation
num_bytes: 838032
num_examples: 2000
download_size: 1179202534
dataset_size: 10050725
- config_name: instance_segmentation
features:
- name: image
dtype: image
- name: annotation
dtype: image
splits:
- name: train
num_bytes: 862611544
num_examples: 20210
- name: test
num_bytes: 212493928
num_examples: 3352
- name: validation
num_bytes: 87502294
num_examples: 2000
download_size: 1197393920
dataset_size: 1162607766
---
# Dataset Card for MIT Scene Parsing Benchmark
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [MIT Scene Parsing Benchmark homepage](http://sceneparsing.csail.mit.edu/)
- **Repository:** [Scene Parsing repository (Caffe/Torch7)](https://github.com/CSAILVision/sceneparsing),[Scene Parsing repository (PyTorch)](https://github.com/CSAILVision/semantic-segmentation-pytorch) and [Instance Segmentation repository](https://github.com/CSAILVision/placeschallenge/tree/master/instancesegmentation)
- **Paper:** [Scene Parsing through ADE20K Dataset](http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf) and [Semantic Understanding of Scenes through ADE20K Dataset](https://arxiv.org/abs/1608.05442)
- **Leaderboard:** [MIT Scene Parsing Benchmark leaderboard](http://sceneparsing.csail.mit.edu/#:~:text=twice%20per%20week.-,leaderboard,-Organizers)
- **Point of Contact:** [Bolei Zhou](mailto:bzhou@ie.cuhk.edu.hk)
### Dataset Summary
Scene parsing is the task of segmenting and parsing an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Scene Parsing Benchmark (SceneParse150) provides a standard training and evaluation platform for the algorithms of scene parsing. The data for this benchmark comes from ADE20K Dataset which contains more than 20K scene-centric images exhaustively annotated with objects and object parts. Specifically, the benchmark is divided into 20K images for training, 2K images for validation, and another batch of held-out images for testing. There are in total 150 semantic categories included for evaluation, which include e.g. sky, road, grass, and discrete objects like person, car, bed. Note that there are non-uniform distribution of objects occuring in the images, mimicking a more natural object occurrence in daily scene.
The goal of this benchmark is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bedThis benchamark is similar to semantic segmentation tasks in COCO and Pascal Dataset, but the data is more scene-centric and with a diverse range of object categories. The data for this benchmark comes from ADE20K Dataset which contains more than 20K scene-centric images exhaustively annotated with objects and object parts.
### Supported Tasks and Leaderboards
- `scene-parsing`: The goal of this task is to segment the whole image densely into semantic classes (image regions), where each pixel is assigned a class label such as the region of *tree* and the region of *building*.
[The leaderboard](http://sceneparsing.csail.mit.edu/#:~:text=twice%20per%20week.-,leaderboard,-Organizers) for this task ranks the models by considering the mean of the pixel-wise accuracy and class-wise IoU as the final score. Pixel-wise accuracy indicates the ratio of pixels which are correctly predicted, while class-wise IoU indicates the Intersection of Union of pixels averaged over all the 150 semantic categories. Refer to the [Development Kit](https://github.com/CSAILVision/sceneparsing) for the detail.
- `instance-segmentation`: The goal of this task is to detect the object instances inside an image and further generate the precise segmentation masks of the objects. Its difference compared to the task of scene parsing is that in scene parsing there is no instance concept for the segmented regions, instead in instance segmentation if there are three persons in the scene, the network is required to segment each one of the person regions. This task doesn't have an active leaderboard. The performance of the instance segmentation algorithms is evaluated by Average Precision (AP, or mAP), following COCO evaluation metrics. For each image, at most 255 top-scoring instance masks are taken across all categories. Each instance mask prediction is only considered if its IoU with ground truth is above a certain threshold. There are 10 IoU thresholds of 0.50:0.05:0.95 for evaluation. The final AP is averaged across 10 IoU thresholds and 100 categories. You can refer to COCO evaluation page for more explanation: http://mscoco.org/dataset/#detections-eval
### Languages
English.
## Dataset Structure
### Data Instances
A data point comprises an image and its annotation mask, which is `None` in the testing set. The `scene_parsing` configuration has an additional `scene_category` field.
#### `scene_parsing`
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=683x512 at 0x1FF32A3EDA0>,
'annotation': <PIL.PngImagePlugin.PngImageFile image mode=L size=683x512 at 0x1FF32E5B978>,
'scene_category': 0
}
```
#### `instance_segmentation`
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=256x256 at 0x20B51B5C400>,
'annotation': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256 at 0x20B57051B38>
}
```
### Data Fields
#### `scene_parsing`
- `image`: A `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]`.
- `annotation`: A `PIL.Image.Image` object containing the annotation mask.
- `scene_category`: A scene category for the image (e.g. `airport_terminal`, `canyon`, `mobile_home`).
> **Note**: annotation masks contain labels ranging from 0 to 150, where 0 refers to "other objects". Those pixels are not considered in the official evaluation. Refer to [this file](https://github.com/CSAILVision/sceneparsing/blob/master/objectInfo150.csv) for the information about the labels of the 150 semantic categories, including indices, pixel ratios and names.
#### `instance_segmentation`
- `image`: A `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]`.
- `annotation`: A `PIL.Image.Image` object containing the annotation mask.
> **Note**: in the instance annotation masks, the R(ed) channel encodes category ID, and the G(reen) channel encodes instance ID. Each object instance has a unique instance ID regardless of its category ID. In the dataset, all images have <256 object instances. Refer to [this file (train split)](https://github.com/CSAILVision/placeschallenge/blob/master/instancesegmentation/instanceInfo100_train.txt) and to [this file (validation split)](https://github.com/CSAILVision/placeschallenge/blob/master/instancesegmentation/instanceInfo100_val.txt) for the information about the labels of the 100 semantic categories. To find the mapping between the semantic categories for `instance_segmentation` and `scene_parsing`, refer to [this file](https://github.com/CSAILVision/placeschallenge/blob/master/instancesegmentation/categoryMapping.txt).
### Data Splits
The data is split into training, test and validation set. The training data contains 20210 images, the testing data contains 3352 images and the validation data contains 2000 images.
## Dataset Creation
### Curation Rationale
The rationale from the paper for the ADE20K dataset from which this benchmark originates:
> Semantic understanding of visual scenes is one of the holy grails of computer vision. Despite efforts of the community in data collection, there are still few image datasets covering a wide range of scenes and object categories with pixel-wise annotations for scene understanding. In this work, we present a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and
in some cases even parts of parts.
> The motivation of this work is to collect a dataset that has densely annotated images (every pixel has a semantic label) with a large and an unrestricted open vocabulary. The
images in our dataset are manually segmented in great detail, covering a diverse set of scenes, object and object part categories. The challenge for collecting such annotations is finding reliable annotators, as well as the fact that labeling is difficult if the class list is not defined in advance. On the other hand, open vocabulary naming also suffers from naming inconsistencies across different annotators. In contrast,
our dataset was annotated by a single expert annotator, providing extremely detailed and exhaustive image annotations. On average, our annotator labeled 29 annotation segments per image, compared to the 16 segments per image labeled by external annotators (like workers from Amazon Mechanical Turk). Furthermore, the data consistency and quality are much higher than that of external annotators.
### Source Data
#### Initial Data Collection and Normalization
Images come from the LabelMe, SUN datasets, and Places and were selected to cover the 900 scene categories defined in the SUN database.
This benchmark was built by selecting the top 150 objects ranked by their total pixel ratios from the ADE20K dataset. As the original images in the ADE20K dataset have various sizes, for simplicity those large-sized images were rescaled to make their minimum heights or widths as 512. Among the 150 objects, there are 35 stuff classes (i.e., wall, sky, road) and 115 discrete objects (i.e., car, person, table). The annotated pixels of the 150 objects occupy 92.75% of all the pixels in the dataset, where the stuff classes occupy 60.92%, and discrete objects occupy 31.83%.
#### Who are the source language producers?
The same as in the LabelMe, SUN datasets, and Places datasets.
### Annotations
#### Annotation process
Annotation process for the ADE20K dataset:
> **Image Annotation.** For our dataset, we are interested in having a diverse set of scenes with dense annotations of all the objects present. Images come from the LabelMe, SUN datasets, and Places and were selected to cover the 900 scene categories defined in the SUN database. Images were annotated by a single expert worker using the LabelMe interface. Fig. 2 shows a snapshot of the annotation interface and one fully segmented image. The worker provided three types of annotations: object segments with names, object parts, and attributes. All object instances are segmented independently so that the dataset could be used to train and evaluate detection or segmentation algorithms. Datasets such as COCO, Pascal or Cityscape start by defining a set of object categories of interest. However, when labeling all the objects in a scene, working with a predefined list of objects is not possible as new categories
appear frequently (see fig. 5.d). Here, the annotator created a dictionary of visual concepts where new classes were added constantly to ensure consistency in object naming. Object parts are associated with object instances. Note that parts can have parts too, and we label these associations as well. For example, the ‘rim’ is a part of a ‘wheel’, which in turn is part of a ‘car’. A ‘knob’ is a part of a ‘door’
that can be part of a ‘cabinet’. The total part hierarchy has a depth of 3. The object and part hierarchy is in the supplementary materials.
> **Annotation Consistency.** Defining a labeling protocol is relatively easy when the labeling task is restricted to a fixed list of object classes, however it becomes challenging when the class list is openended. As the goal is to label all the objects within each image, the list of classes grows unbounded. >Many object classes appear only a few times across the entire collection of images. However, those rare >object classes cannot be ignored as they might be important elements for the interpretation of the scene. >Labeling in these conditions becomes difficult because we need to keep a growing list of all the object >classes in order to have a consistent naming across the entire dataset. Despite the annotator’s best effort, >the process is not free of noise. To analyze the annotation consistency we took a subset of 61 randomly >chosen images from the validation set, then asked our annotator to annotate them again (there is a time difference of six months). One expects that there are some differences between the two annotations. A few examples are shown in Fig 3. On average, 82.4% of the pixels got the same label. The remaining 17.6% of pixels had some errors for which we grouped into three error types as follows:
>
> • Segmentation quality: Variations in the quality of segmentation and outlining of the object boundary. One typical source of error arises when segmenting complex objects such as buildings and trees, which can be segmented with different degrees of precision. 5.7% of the pixels had this type of error.
>
> • Object naming: Differences in object naming (due to ambiguity or similarity between concepts, for instance calling a big car a ‘car’ in one segmentation and a ‘truck’ in the another one, or a ‘palm tree’ a‘tree’. 6.0% of the pixels had naming issues. These errors can be reduced by defining a very precise terminology, but this becomes much harder with a large growing vocabulary.
>
> • Segmentation quantity: Missing objects in one of the two segmentations. There is a very large number of objects in each image and some images might be annotated more thoroughly than others. For example, in the third column of Fig 3 the annotator missed some small objects in different annotations. 5.9% of the pixels are due to missing labels. A similar issue existed in segmentation datasets such as the Berkeley Image segmentation dataset.
>
> The median error values for the three error types are: 4.8%, 0.3% and 2.6% showing that the mean value is dominated by a few images, and that the most common type of error is segmentation quality.
To further compare the annotation done by our single expert annotator and the AMT-like annotators, 20 images
from the validation set are annotated by two invited external annotators, both with prior experience in image labeling. The first external annotator had 58.5% of inconsistent pixels compared to the segmentation provided by our annotator, and the second external annotator had 75% of the inconsistent pixels. Many of these inconsistencies are due to the poor quality of the segmentations provided by external annotators (as it has been observed with AMT which requires multiple verification steps for quality control). For the
best external annotator (the first one), 7.9% of pixels have inconsistent segmentations (just slightly worse than our annotator), 14.9% have inconsistent object naming and 35.8% of the pixels correspond to missing objects, which is due to the much smaller number of objects annotated by the external annotator in comparison with the ones annotated by our expert annotator. The external annotators labeled on average 16 segments per image while our annotator provided 29 segments per image.
#### Who are the annotators?
Three expert annotators and the AMT-like annotators.
### 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
Refer to the `Annotation Consistency` subsection of `Annotation Process`.
## Additional Information
### Dataset Curators
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso and Antonio Torralba.
### Licensing Information
The MIT Scene Parsing Benchmark dataset is licensed under a [BSD 3-Clause License](https://github.com/CSAILVision/sceneparsing/blob/master/LICENSE).
### Citation Information
```bibtex
@inproceedings{zhou2017scene,
title={Scene Parsing through ADE20K Dataset},
author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2017}
}
@article{zhou2016semantic,
title={Semantic understanding of scenes through the ade20k dataset},
author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
journal={arXiv preprint arXiv:1608.05442},
year={2016}
}
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |