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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': airplane |
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'1': alarm clock |
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'2': angel |
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'3': ant |
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'4': apple |
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'5': arm |
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'6': armchair |
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'7': ashtray |
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'8': axe |
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'9': backpack |
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'10': banana |
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'11': barn |
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'12': baseball bat |
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'13': basket |
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'14': bathtub |
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'15': bear (animal) |
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'16': bed |
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'17': bee |
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'18': beer-mug |
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'19': bell |
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'20': bench |
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'21': bicycle |
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'22': binoculars |
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'23': blimp |
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'24': book |
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'25': bookshelf |
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'26': boomerang |
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'27': bottle opener |
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'28': bowl |
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'29': brain |
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'30': bread |
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'31': bridge |
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'32': bulldozer |
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'33': bus |
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'34': bush |
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'35': butterfly |
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'36': cabinet |
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'37': cactus |
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'38': cake |
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'39': calculator |
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'40': camel |
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'41': camera |
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'42': candle |
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'43': cannon |
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'44': canoe |
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'45': car (sedan) |
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'46': carrot |
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'47': castle |
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'48': cat |
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'49': cell phone |
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'50': chair |
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'51': chandelier |
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'52': church |
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'53': cigarette |
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'54': cloud |
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'55': comb |
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'56': computer monitor |
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'57': computer-mouse |
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'58': couch |
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'59': cow |
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'60': crab |
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'61': crane (machine) |
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'62': crocodile |
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'63': crown |
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'64': cup |
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'65': diamond |
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'66': dog |
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'67': dolphin |
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'68': donut |
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'69': door |
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'70': door handle |
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'71': dragon |
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'72': duck |
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'73': ear |
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'74': elephant |
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'75': envelope |
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'76': eye |
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'77': eyeglasses |
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'78': face |
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'79': fan |
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'80': feather |
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'81': fire hydrant |
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'82': fish |
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'83': flashlight |
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'84': floor lamp |
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'85': flower with stem |
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'86': flying bird |
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'87': flying saucer |
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'88': foot |
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'89': fork |
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'90': frog |
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'91': frying-pan |
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'92': giraffe |
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'93': grapes |
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'94': grenade |
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'95': guitar |
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'96': hamburger |
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'97': hammer |
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'98': hand |
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'99': harp |
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'100': hat |
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'101': head |
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'102': head-phones |
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'103': hedgehog |
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'104': helicopter |
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'105': helmet |
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'106': horse |
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'107': hot air balloon |
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'108': hot-dog |
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'109': hourglass |
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'110': house |
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'111': human-skeleton |
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'112': ice-cream-cone |
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'113': ipod |
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'114': kangaroo |
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'115': key |
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'116': keyboard |
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'117': knife |
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'118': ladder |
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'119': laptop |
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'120': leaf |
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'121': lightbulb |
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'122': lighter |
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'123': lion |
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'124': lobster |
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'125': loudspeaker |
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'126': mailbox |
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'127': megaphone |
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'128': mermaid |
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'129': microphone |
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'130': microscope |
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'131': monkey |
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'132': moon |
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'133': mosquito |
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'134': motorbike |
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'135': mouse (animal) |
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'136': mouth |
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'137': mug |
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'138': mushroom |
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'139': nose |
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'140': octopus |
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'141': owl |
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'142': palm tree |
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'143': panda |
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'144': paper clip |
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'145': parachute |
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'146': parking meter |
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'147': parrot |
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'148': pear |
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'149': pen |
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'150': penguin |
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'151': person sitting |
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'152': person walking |
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'153': piano |
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'154': pickup truck |
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'155': pig |
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'156': pigeon |
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'157': pineapple |
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'158': pipe (for smoking) |
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'159': pizza |
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'160': potted plant |
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'161': power outlet |
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'162': present |
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'163': pretzel |
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'164': pumpkin |
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'165': purse |
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'166': rabbit |
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'167': race car |
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'168': radio |
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'169': rainbow |
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'170': revolver |
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'171': rifle |
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'172': rollerblades |
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'173': rooster |
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'174': sailboat |
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'175': santa claus |
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'176': satellite |
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'177': satellite dish |
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'178': saxophone |
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'179': scissors |
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'180': scorpion |
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'181': screwdriver |
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'182': sea turtle |
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'183': seagull |
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'184': shark |
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'185': sheep |
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'186': ship |
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'187': shoe |
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'188': shovel |
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'189': skateboard |
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'190': skull |
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'191': skyscraper |
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'192': snail |
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'193': snake |
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'194': snowboard |
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'195': snowman |
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'196': socks |
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'197': space shuttle |
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'198': speed-boat |
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'199': spider |
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'200': sponge bob |
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'201': spoon |
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'202': squirrel |
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'203': standing bird |
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'204': stapler |
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'205': strawberry |
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'206': streetlight |
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'207': submarine |
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'208': suitcase |
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'209': sun |
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'210': suv |
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'211': swan |
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'212': sword |
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'213': syringe |
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'214': t-shirt |
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'215': table |
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'216': tablelamp |
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'217': teacup |
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'218': teapot |
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'219': teddy-bear |
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'220': telephone |
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'221': tennis-racket |
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'222': tent |
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'223': tiger |
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'224': tire |
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'225': toilet |
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'226': tomato |
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'227': tooth |
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'228': toothbrush |
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'229': tractor |
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'230': traffic light |
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'231': train |
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'232': tree |
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'233': trombone |
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'234': trousers |
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'235': truck |
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'236': trumpet |
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'237': tv |
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'238': umbrella |
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'239': van |
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'240': vase |
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'241': violin |
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'242': walkie talkie |
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'243': wheel |
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'244': wheelbarrow |
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'245': windmill |
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'246': wine-bottle |
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'247': wineglass |
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'248': wrist-watch |
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'249': zebra |
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splits: |
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- name: train |
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num_bytes: 480609419.0 |
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num_examples: 16000 |
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- name: validation |
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num_bytes: 59693656.0 |
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num_examples: 2000 |
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- name: test |
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num_bytes: 60354461.0 |
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num_examples: 2000 |
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download_size: 589082694 |
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dataset_size: 600657536.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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--- |
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# Dataset Card for TU Berline Dataset |
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This dataset card aims to provide comprehensive information about the TU Berlin dataset, a collection of hand-drawn sketches used for training and evaluating sketch classification models. |
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## Dataset Details |
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### Dataset Description |
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The TU Berlin dataset is a large-scale collection of hand-drawn sketches curated by the research team at TU Berlin. The dataset includes 20,000 unique sketches across 250 object categories, contributed by participants from around the world. The primary purpose of this dataset is to facilitate research in the field of computer vision, particularly for tasks related to sketch recognition and classification. |
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- **Curated by:** TU Berlin research team |
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- **Shared by [optional]:** TU Berlin |
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### Dataset Sources |
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- **Source:** [TU Berlin Dataset Source](https://cybertron.cg.tu-berlin.de/eitz/projects/classifysketch/) |
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- **Paper:** [TU Berlin Dataset Paper](https://cybertron.cg.tu-berlin.de/eitz/pdf/2012_siggraph_classifysketch.pdf) |
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## Uses |
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### Direct Use |
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The dataset is intended for use in developing and evaluating sketch recognition algorithms. It is suitable for tasks such as: |
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- Training sketch classification models |
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- Evaluating the performance of sketch recognition systems |
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- Conducting research in computer vision and machine learning related to hand-drawn images |
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### Out-of-Scope Use |
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The dataset is not suitable for use cases that require high-resolution images or photographs. It is also not intended for tasks unrelated to sketch recognition, such as natural image classification. |
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## Dataset Structure |
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The dataset is organized into categories, each containing a collection of hand-drawn sketches. Each sketch is a black-and-white image representing an object from one of the predefined categories. |
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- **Number of Categories:** 250 |
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- **Number of Sketches:** 20,000 |
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### Dataset Splits |
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I downloaded the TU Berlin dataset and split it into train set, validation set, and test set. |
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- **Train Set:** |
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- **Number of Examples:** 16,000 |
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- **Size:** 480,609,419 bytes |
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- **Validation Set:** |
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- **Number of Examples:** 2,000 |
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- **Size:** 59,693,656 bytes |
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- **Test Set:** |
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- **Number of Examples:** 2,000 |
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- **Size:** 60,354,461 bytes |
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- **Download Size:** 589,085,954 bytes |
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- **Total Dataset Size:** 600,657,536 bytes |
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The data was split using the following code: |
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```python |
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from sklearn.model_selection import train_test_split |
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train_data, temp_data = train_test_split(metadata, test_size=0.2, random_state=42) |
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val_data, test_data = train_test_split(temp_data, test_size=0.5, random_state=42) |
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``` |
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## Citation |
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**BibTeX:** |
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```bibtex |
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@article{eitz2012hdhso, |
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author={Eitz, Mathias and Hays, James and Alexa, Marc}, |
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title={How Do Humans Sketch Objects?}, |
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journal={ACM Trans. Graph. (Proc. SIGGRAPH)}, |
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year={2012}, |
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volume={31}, |
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number={4}, |
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pages = {44:1--44:10} |
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} |
