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--- |
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dataset_info: |
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- config_name: I2E-CIFAR10 |
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features: |
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- name: file_path |
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dtype: string |
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- name: data |
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dtype: binary |
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splits: |
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- name: train |
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num_bytes: 1646538890 |
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num_examples: 50000 |
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- name: validation |
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num_examples: 10000 |
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download_size: 464478602 |
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dataset_size: 1975837780 |
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- config_name: I2E-CIFAR100 |
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- name: data |
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dtype: binary |
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splits: |
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- name: train |
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num_bytes: 1646583890 |
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num_examples: 50000 |
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- name: validation |
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num_bytes: 329307890 |
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num_examples: 10000 |
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download_size: 462298257 |
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dataset_size: 1975891780 |
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- config_name: I2E-Caltech101 |
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features: |
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- name: file_path |
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dtype: string |
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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': Faces |
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'1': Faces_easy |
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'2': Leopards |
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'3': Motorbikes |
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'4': accordion |
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'5': airplanes |
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'6': anchor |
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'7': ant |
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'8': barrel |
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'9': bass |
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'10': beaver |
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'11': binocular |
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'12': bonsai |
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'13': brain |
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'14': brontosaurus |
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'15': buddha |
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'16': butterfly |
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'17': camera |
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'18': cannon |
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'19': car_side |
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'20': ceiling_fan |
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'21': cellphone |
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'22': chair |
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'23': chandelier |
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'24': cougar_body |
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'25': cougar_face |
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'26': crab |
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'27': crayfish |
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'28': crocodile |
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'29': crocodile_head |
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'30': cup |
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'31': dalmatian |
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'32': dollar_bill |
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'33': dolphin |
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'34': dragonfly |
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'35': electric_guitar |
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'36': elephant |
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'37': emu |
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'38': euphonium |
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'39': ewer |
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'40': ferry |
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'41': flamingo |
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'42': flamingo_head |
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'43': garfield |
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'44': gerenuk |
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'45': gramophone |
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'46': grand_piano |
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'47': hawksbill |
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'48': headphone |
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'49': hedgehog |
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'50': helicopter |
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'51': ibis |
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'52': inline_skate |
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'53': joshua_tree |
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'54': kangaroo |
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'55': ketch |
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'56': lamp |
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'57': laptop |
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'58': llama |
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'59': lobster |
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'60': lotus |
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'61': mandolin |
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'62': mayfly |
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'63': menorah |
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'64': metronome |
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'65': minaret |
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'66': nautilus |
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'67': octopus |
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'68': okapi |
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'69': pagoda |
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'70': panda |
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'71': pigeon |
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'72': pizza |
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'73': platypus |
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'74': pyramid |
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'75': revolver |
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'76': rhino |
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'77': rooster |
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'78': saxophone |
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'79': schooner |
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'80': scissors |
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'81': scorpion |
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'82': sea_horse |
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'83': snoopy |
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'84': soccer_ball |
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'85': stapler |
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'86': starfish |
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'87': stegosaurus |
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'88': stop_sign |
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'89': strawberry |
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'90': sunflower |
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'91': tick |
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'92': trilobite |
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'93': umbrella |
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'94': watch |
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'95': water_lilly |
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'96': wheelchair |
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'97': wild_cat |
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'98': windsor_chair |
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'99': wrench |
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'100': yin_yang |
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- name: data |
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dtype: binary |
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splits: |
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- name: train |
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num_bytes: 872272607 |
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num_examples: 8677 |
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download_size: 344357976 |
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dataset_size: 872272607 |
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- config_name: I2E-Caltech256 |
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features: |
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- name: file_path |
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dtype: string |
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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': 001.ak47 |
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'1': 002.american-flag |
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'2': 003.backpack |
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'3': 004.baseball-bat |
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'4': 005.baseball-glove |
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'5': 006.basketball-hoop |
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'6': 007.bat |
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'7': 008.bathtub |
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'8': 009.bear |
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'9': 010.beer-mug |
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'10': 011.billiards |
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'11': 012.binoculars |
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'12': 013.birdbath |
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'13': 014.blimp |
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'14': 015.bonsai-101 |
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'15': 016.boom-box |
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'16': 017.bowling-ball |
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'17': 018.bowling-pin |
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'18': 019.boxing-glove |
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'19': 020.brain-101 |
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'20': 021.breadmaker |
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'21': 022.buddha-101 |
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'22': 023.bulldozer |
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'23': 024.butterfly |
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'24': 025.cactus |
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'25': 026.cake |
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'26': 027.calculator |
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'27': 028.camel |
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'28': 029.cannon |
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'29': 030.canoe |
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'30': 031.car-tire |
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'31': 032.cartman |
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'32': 033.cd |
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'33': 034.centipede |
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'34': 035.cereal-box |
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'35': 036.chandelier-101 |
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'36': 037.chess-board |
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'37': 038.chimp |
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'38': 039.chopsticks |
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'39': 040.cockroach |
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'40': 041.coffee-mug |
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'41': 042.coffin |
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'42': 043.coin |
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'43': 044.comet |
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'44': 045.computer-keyboard |
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'45': 046.computer-monitor |
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'46': 047.computer-mouse |
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'47': 048.conch |
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'48': 049.cormorant |
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'49': 050.covered-wagon |
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'50': 051.cowboy-hat |
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'51': 052.crab-101 |
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'52': 053.desk-globe |
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'53': 054.diamond-ring |
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'54': 055.dice |
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'55': 056.dog |
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'56': 057.dolphin-101 |
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'57': 058.doorknob |
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'58': 059.drinking-straw |
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'59': 060.duck |
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'60': 061.dumb-bell |
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'61': 062.eiffel-tower |
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'62': 063.electric-guitar-101 |
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'63': 064.elephant-101 |
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'64': 065.elk |
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'65': 066.ewer-101 |
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'66': 067.eyeglasses |
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'67': 068.fern |
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'68': 069.fighter-jet |
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'69': 070.fire-extinguisher |
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'70': 071.fire-hydrant |
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'71': 072.fire-truck |
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'72': 073.fireworks |
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'73': 074.flashlight |
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'74': 075.floppy-disk |
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'75': 076.football-helmet |
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'76': 077.french-horn |
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'77': 078.fried-egg |
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'78': 079.frisbee |
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'79': 080.frog |
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'80': 081.frying-pan |
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'81': 082.galaxy |
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'82': 083.gas-pump |
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'83': 084.giraffe |
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'84': 085.goat |
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'85': 086.golden-gate-bridge |
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'86': 087.goldfish |
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'87': 088.golf-ball |
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'88': 089.goose |
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'89': 090.gorilla |
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'90': 091.grand-piano-101 |
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'91': 092.grapes |
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'92': 093.grasshopper |
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'93': 094.guitar-pick |
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'94': 095.hamburger |
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'95': 096.hammock |
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'96': 097.harmonica |
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'97': 098.harp |
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'98': 099.harpsichord |
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'99': 100.hawksbill-101 |
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'100': 101.head-phones |
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'101': 102.helicopter-101 |
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'102': 103.hibiscus |
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'103': 104.homer-simpson |
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'104': 105.horse |
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'105': 106.horseshoe-crab |
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'106': 107.hot-air-balloon |
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'107': 108.hot-dog |
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'108': 109.hot-tub |
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'109': 110.hourglass |
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'110': 111.house-fly |
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'111': 112.human-skeleton |
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'112': 113.hummingbird |
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'113': 114.ibis-101 |
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'114': 115.ice-cream-cone |
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'115': 116.iguana |
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'116': 117.ipod |
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'117': 118.iris |
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'118': 119.jesus-christ |
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'119': 120.joy-stick |
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'120': 121.kangaroo-101 |
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'121': 122.kayak |
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'122': 123.ketch-101 |
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'123': 124.killer-whale |
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'124': 125.knife |
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'125': 126.ladder |
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'126': 127.laptop-101 |
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'127': 128.lathe |
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'128': 129.leopards-101 |
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'129': 130.license-plate |
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'130': 131.lightbulb |
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'131': 132.light-house |
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'132': 133.lightning |
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'133': 134.llama-101 |
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'134': 135.mailbox |
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'135': 136.mandolin |
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'136': 137.mars |
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'137': 138.mattress |
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'138': 139.megaphone |
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'139': 140.menorah-101 |
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'140': 141.microscope |
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'141': 142.microwave |
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'142': 143.minaret |
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'143': 144.minotaur |
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'144': 145.motorbikes-101 |
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'145': 146.mountain-bike |
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'146': 147.mushroom |
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'147': 148.mussels |
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'148': 149.necktie |
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'149': 150.octopus |
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'150': 151.ostrich |
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'151': 152.owl |
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'152': 153.palm-pilot |
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'153': 154.palm-tree |
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'154': 155.paperclip |
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'155': 156.paper-shredder |
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'156': 157.pci-card |
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'157': 158.penguin |
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'158': 159.people |
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'159': 160.pez-dispenser |
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'160': 161.photocopier |
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'161': 162.picnic-table |
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'162': 163.playing-card |
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'163': 164.porcupine |
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'164': 165.pram |
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'165': 166.praying-mantis |
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'166': 167.pyramid |
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'167': 168.raccoon |
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'168': 169.radio-telescope |
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'169': 170.rainbow |
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'170': 171.refrigerator |
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'171': 172.revolver-101 |
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'172': 173.rifle |
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'173': 174.rotary-phone |
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'174': 175.roulette-wheel |
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'175': 176.saddle |
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'176': 177.saturn |
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|
'177': 178.school-bus |
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'178': 179.scorpion-101 |
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|
'179': 180.screwdriver |
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|
'180': 181.segway |
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|
'181': 182.self-propelled-lawn-mower |
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|
'182': 183.sextant |
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|
'183': 184.sheet-music |
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'184': 185.skateboard |
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'185': 186.skunk |
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|
'186': 187.skyscraper |
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'187': 188.smokestack |
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'188': 189.snail |
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'189': 190.snake |
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'190': 191.sneaker |
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'191': 192.snowmobile |
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'192': 193.soccer-ball |
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'193': 194.socks |
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'194': 195.soda-can |
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'195': 196.spaghetti |
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'196': 197.speed-boat |
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'197': 198.spider |
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'198': 199.spoon |
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'199': 200.stained-glass |
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'200': 201.starfish-101 |
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'201': 202.steering-wheel |
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'202': 203.stirrups |
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'203': 204.sunflower-101 |
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'204': 205.superman |
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'205': 206.sushi |
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'206': 207.swan |
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'207': 208.swiss-army-knife |
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'208': 209.sword |
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'209': 210.syringe |
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'210': 211.tambourine |
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'211': 212.teapot |
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'212': 213.teddy-bear |
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'213': 214.teepee |
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'214': 215.telephone-box |
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'215': 216.tennis-ball |
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'216': 217.tennis-court |
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'217': 218.tennis-racket |
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'218': 219.theodolite |
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'219': 220.toaster |
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'220': 221.tomato |
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'221': 222.tombstone |
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'222': 223.top-hat |
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|
'223': 224.touring-bike |
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'224': 225.tower-pisa |
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|
'225': 226.traffic-light |
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|
'226': 227.treadmill |
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|
'227': 228.triceratops |
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|
'228': 229.tricycle |
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'229': 230.trilobite-101 |
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|
'230': 231.tripod |
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|
'231': 232.t-shirt |
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|
'232': 233.tuning-fork |
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'233': 234.tweezer |
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'234': 235.umbrella-101 |
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'235': 236.unicorn |
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'999': n15075141 |
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- name: data |
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dtype: binary |
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splits: |
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- name: train |
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num_bytes: 128798692994 |
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num_examples: 1281167 |
|
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- name: validation |
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num_bytes: 5027050000 |
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|
num_examples: 50000 |
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|
download_size: 57961329620 |
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dataset_size: 133825742994 |
|
|
- config_name: I2E-MNIST |
|
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features: |
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- name: file_path |
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dtype: string |
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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': '0' |
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'1': '1' |
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'2': '2' |
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'3': '3' |
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'4': '4' |
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'5': '5' |
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'6': '6' |
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'7': '7' |
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'8': '8' |
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'9': '9' |
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- name: data |
|
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dtype: binary |
|
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splits: |
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|
- name: train |
|
|
num_bytes: 132648890 |
|
|
num_examples: 60000 |
|
|
- name: validation |
|
|
num_bytes: 22098890 |
|
|
num_examples: 10000 |
|
|
download_size: 60473109 |
|
|
dataset_size: 154747780 |
|
|
- config_name: I2E-Mini-ImageNet |
|
|
features: |
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|
- name: file_path |
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|
dtype: string |
|
|
- name: label |
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dtype: |
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class_label: |
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names: |
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'0': n01532829 |
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'1': n01558993 |
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'69': n03908618 |
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'79': n04275548 |
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'80': n04296562 |
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'82': n04418357 |
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'83': n04435653 |
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'87': n04522168 |
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'89': n04604644 |
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'90': n04612504 |
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'92': n07584110 |
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'93': n07613480 |
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'94': n07697537 |
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'97': n09256479 |
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'98': n13054560 |
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'99': n13133613 |
|
|
- name: data |
|
|
dtype: binary |
|
|
splits: |
|
|
- name: train |
|
|
num_bytes: 6031941884 |
|
|
num_examples: 60000 |
|
|
download_size: 2568434568 |
|
|
dataset_size: 6031941884 |
|
|
configs: |
|
|
- config_name: I2E-CIFAR10 |
|
|
data_files: |
|
|
- split: train |
|
|
path: I2E-CIFAR10/train-* |
|
|
- split: validation |
|
|
path: I2E-CIFAR10/validation-* |
|
|
- config_name: I2E-CIFAR100 |
|
|
data_files: |
|
|
- split: train |
|
|
path: I2E-CIFAR100/train-* |
|
|
- split: validation |
|
|
path: I2E-CIFAR100/validation-* |
|
|
- config_name: I2E-Caltech101 |
|
|
data_files: |
|
|
- split: train |
|
|
path: I2E-Caltech101/train-* |
|
|
- config_name: I2E-Caltech256 |
|
|
data_files: |
|
|
- split: train |
|
|
path: I2E-Caltech256/train-* |
|
|
- config_name: I2E-FashionMNIST |
|
|
data_files: |
|
|
- split: train |
|
|
path: I2E-FashionMNIST/train-* |
|
|
- split: validation |
|
|
path: I2E-FashionMNIST/validation-* |
|
|
- config_name: I2E-ImageNet |
|
|
data_files: |
|
|
- split: train |
|
|
path: I2E-ImageNet/train-* |
|
|
- split: validation |
|
|
path: I2E-ImageNet/validation-* |
|
|
- config_name: I2E-MNIST |
|
|
data_files: |
|
|
- split: train |
|
|
path: I2E-MNIST/train-* |
|
|
- split: validation |
|
|
path: I2E-MNIST/validation-* |
|
|
- config_name: I2E-Mini-ImageNet |
|
|
data_files: |
|
|
- split: train |
|
|
path: I2E-Mini-ImageNet/train-* |
|
|
license: mit |
|
|
task_categories: |
|
|
- image-classification |
|
|
- video-classification |
|
|
tags: |
|
|
- neuromorphic |
|
|
- snn |
|
|
- spiking neural networks |
|
|
- event |
|
|
- dvs |
|
|
- biology |
|
|
- pytorch |
|
|
- imagenet |
|
|
- cifar10 |
|
|
- cifar100 |
|
|
- caltech101 |
|
|
- caltech256 |
|
|
- mnist |
|
|
- fashionmnist |
|
|
- mini-imagenet |
|
|
pretty_name: I2E Neuromorphic Dataset |
|
|
language: |
|
|
- en |
|
|
--- |
|
|
|
|
|
|
|
|
<div align="center"> |
|
|
|
|
|
<h1>I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks</h1> |
|
|
|
|
|
[](https://arxiv.org/abs/2511.08065) |
|
|
[](https://aaai.org/) |
|
|
[](https://scholar.google.com/scholar?cluster=1814482600796011970) |
|
|
[](https://github.com/Ruichen0424/I2E) |
|
|
|
|
|
[](https://huggingface.co/papers/2511.08065) |
|
|
[](https://huggingface.co/Ruichen0424/I2E) |
|
|
</div> |
|
|
|
|
|
## 🚀 Introduction |
|
|
|
|
|
This repository hosts the **I2E-Datasets**, a comprehensive suite of neuromorphic datasets generated using the **I2E (Image-to-Event)** framework. This work has been accepted for **Oral Presentation at AAAI 2026**. |
|
|
|
|
|
**I2E** bridges the data scarcity gap in **Neuromorphic Computing** and **Spiking Neural Networks (SNNs)**. By simulating microsaccadic eye movements via highly parallelized convolution, I2E converts static images into high-fidelity event streams in real-time (>300x faster than prior methods). |
|
|
|
|
|
## 👁️ Visualization |
|
|
|
|
|
The following comparisons illustrate the high-fidelity conversion from static RGB images to dynamic event streams using I2E. |
|
|
|
|
|
<table border="0" style="width: 100%"> |
|
|
<tr> |
|
|
<td width="25%" align="center"><img src="./assets/original_1.jpg" alt="Original 1" style="width:100%"></td> |
|
|
<td width="25%" align="center"><img src="./assets/converted_1.gif" alt="Converted 1" style="width:100%"></td> |
|
|
<td width="25%" align="center"><img src="./assets/original_2.jpg" alt="Original 2" style="width:100%"></td> |
|
|
<td width="25%" align="center"><img src="./assets/converted_2.gif" alt="Converted 2" style="width:100%"></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td width="25%" align="center"><img src="./assets/original_3.jpg" alt="Original 3" style="width:100%"></td> |
|
|
<td width="25%" align="center"><img src="./assets/converted_3.gif" alt="Converted 3" style="width:100%"></td> |
|
|
<td width="25%" align="center"><img src="./assets/original_4.jpg" alt="Original 4" style="width:100%"></td> |
|
|
<td width="25%" align="center"><img src="./assets/converted_4.gif" alt="Converted 4" style="width:100%"></td> |
|
|
</tr> |
|
|
</table> |
|
|
|
|
|
*More visualization comparisons can be found in [Visualization.md](./Visualization.md).* |
|
|
|
|
|
## 📦 Dataset Catalog |
|
|
|
|
|
We provide a comprehensive collection of standard benchmarks converted into event streams via the I2E algorithm. |
|
|
|
|
|
### 1. Standard Benchmarks (Classification) |
|
|
| Config Name | Original Source | Resolution $(H, W)$ | I2E Ratio | Event Rate | Samples (Train/Val) | |
|
|
| :--- | :--- | :--- | :--- | :--- | :--- | |
|
|
| **`I2E-CIFAR10`** | CIFAR-10 | 128 x 128 | 0.07 | 5.86% | 50k / 10k | |
|
|
| **`I2E-CIFAR100`** | CIFAR-100 | 128 x 128 | 0.07 | 5.76% | 50k / 10k | |
|
|
| **`I2E-ImageNet`** | ILSVRC2012 | 224 x 224 | 0.12 | 6.66% | 1.28M / 50k | |
|
|
|
|
|
### 2. Transfer Learning & Fine-grained |
|
|
| Config Name | Original Source | Resolution $(H, W)$ | I2E Ratio | Event Rate | Samples | |
|
|
| :--- | :--- | :--- | :--- | :--- | :--- | |
|
|
| **`I2E-Caltech101`** | Caltech-101 | 224 x 224 | 0.12 | 6.25% | 8.677k | |
|
|
| **`I2E-Caltech256`** | Caltech-256 | 224 x 224 | 0.12 | 6.04% | 30.607k | |
|
|
| **`I2E-Mini-ImageNet`**| Mini-ImageNet | 224 x 224 | 0.12 | 6.65% | 60k | |
|
|
|
|
|
### 3. Small Scale / Toy |
|
|
| Config Name | Original Source | Resolution $(H, W)$ | I2E Ratio | Event Rate | Samples | |
|
|
| :--- | :--- | :--- | :--- | :--- | :--- | |
|
|
| **`I2E-MNIST`** | MNIST | 32 x 32 | 0.10 | 9.56% | 60k / 10k | |
|
|
| **`I2E-FashionMNIST`** | Fashion-MNIST | 32 x 32 | 0.15 | 10.76% | 60k / 10k | |
|
|
|
|
|
> 🔜 **Coming Soon:** Object Detection and Semantic Segmentation datasets. |
|
|
|
|
|
## 🛠️ Preprocessing Protocol |
|
|
|
|
|
To ensure reproducibility, we specify the exact data augmentation pipeline applied to the static images **before** I2E conversion. |
|
|
|
|
|
The `(H, W)` in the code below corresponds to the "Resolution" column in the Dataset Catalog above. |
|
|
|
|
|
```python |
|
|
from torchvision.transforms import v2 |
|
|
|
|
|
# Standard Pre-processing Pipeline used for I2E generation |
|
|
transform_train = v2.Compose([ |
|
|
# Ensure 3-channel RGB (crucial for grayscale datasets like MNIST) |
|
|
v2.Lambda(lambda x: x.convert('RGB')), |
|
|
v2.PILToTensor(), |
|
|
v2.Resize((H, W), interpolation=v2.InterpolationMode.BICUBIC), |
|
|
v2.ToDtype(torch.float32, scale=True), |
|
|
]) |
|
|
```` |
|
|
|
|
|
## 💻 Usage |
|
|
|
|
|
### 🚀 Quick Start |
|
|
|
|
|
You **do not** need to download any extra scripts. Just copy the code below. It handles the binary unpacking (converting Parquet bytes to PyTorch Tensors) automatically. |
|
|
|
|
|
```python |
|
|
import io |
|
|
import torch |
|
|
import numpy as np |
|
|
from datasets import load_dataset |
|
|
from torch.utils.data import Dataset, DataLoader |
|
|
|
|
|
# ================================================================== |
|
|
# 1. Core Decoding Function (Handles the binary packing) |
|
|
# ================================================================== |
|
|
def unpack_event_data(item, use_io=True): |
|
|
""" |
|
|
Decodes the custom binary format: |
|
|
Header (8 bytes) -> Shape (T, C, H, W) -> Body (Packed Bits) |
|
|
""" |
|
|
if use_io: |
|
|
with io.BytesIO(item['data']) as f: |
|
|
raw_data = np.load(f) |
|
|
else: |
|
|
raw_data = np.load(item) |
|
|
|
|
|
header_size = 4 * 2 # Parse Header (First 8 bytes for 4 uint16 shape values) |
|
|
shape_header = raw_data[:header_size].view(np.uint16) |
|
|
original_shape = tuple(shape_header) # Returns (T, C, H, W) |
|
|
|
|
|
packed_body = raw_data[header_size:] # Parse Body & Bit-unpacking |
|
|
unpacked = np.unpackbits(packed_body) |
|
|
|
|
|
num_elements = np.prod(original_shape) # Extract valid bits (Handle padding) |
|
|
event_flat = unpacked[:num_elements] |
|
|
event_data = event_flat.reshape(original_shape).astype(np.float32).copy() |
|
|
|
|
|
return torch.from_numpy(event_data) |
|
|
|
|
|
# ================================================================== |
|
|
# 2. Dataset Wrapper |
|
|
# ================================================================== |
|
|
class I2E_Dataset(Dataset): |
|
|
def __init__(self, cache_dir, config_name, split='train', transform=None, target_transform=None): |
|
|
print(f"🚀 Loading {config_name} [{split}] from Hugging Face...") |
|
|
self.ds = load_dataset('UESTC-BICS/I2E', config_name, split=split, cache_dir=cache_dir, keep_in_memory=False) |
|
|
self.transform = transform |
|
|
self.target_transform = target_transform |
|
|
|
|
|
def __len__(self): |
|
|
return len(self.ds) |
|
|
|
|
|
def __getitem__(self, idx): |
|
|
item = self.ds[idx] |
|
|
event = unpack_event_data(item) |
|
|
label = item['label'] |
|
|
if self.transform: |
|
|
event = self.transform(event) |
|
|
if self.target_transform: |
|
|
label = self.target_transform(label) |
|
|
return event, label |
|
|
|
|
|
# ================================================================== |
|
|
# 3. Run Example |
|
|
# ================================================================== |
|
|
if __name__ == "__main__": |
|
|
import os |
|
|
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' # Use HF mirror server in some regions |
|
|
|
|
|
DATASET_NAME = 'I2E-CIFAR10' # Choose your config: 'I2E-CIFAR10', 'I2E-ImageNet', etc. |
|
|
MODEL_PATH = 'Your cache path here' # e.g., './hf_datasets_cache/' |
|
|
|
|
|
train_dataset = I2E_Dataset(MODEL_PATH, DATASET_NAME, split='train') |
|
|
val_dataset = I2E_Dataset(MODEL_PATH, DATASET_NAME, split='validation') |
|
|
|
|
|
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=32, persistent_workers=True) |
|
|
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=32, persistent_workers=True) |
|
|
|
|
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events, labels = next(iter(train_loader)) |
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print(f"✅ Loaded Batch Shape: {events.shape}") # Expect: [32, T, 2, H, W] |
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print(f"✅ Labels: {labels}") |
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``` |
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|
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## 🏆 Results (SOTA) |
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|
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Our I2E-pretraining sets new benchmarks for Sim-to-Real transfer on **CIFAR10-DVS**. |
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|
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<table border="1"> |
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<tr> |
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<th>Dataset</th> |
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|
<th align="center">Architecture</th> |
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|
<th align="center">Method</th> |
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|
<th align="center">Top-1 Acc</th> |
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</tr> |
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|
<!-- CIFAR10-DVS --> |
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<tr> |
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|
<td rowspan="3" align="center" style="vertical-align: middle;"><strong>CIFAR10-DVS</strong><br>(Real)</td> |
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|
<td align="center" style="vertical-align: middle;">MS-ResNet18</td> |
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|
<td align="center" style="vertical-align: middle;">Baseline</td> |
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|
<td align="center" style="vertical-align: middle;">65.6%</td> |
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</tr> |
|
|
<tr> |
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|
<td align="center" style="vertical-align: middle;">MS-ResNet18</td> |
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|
<td align="center" style="vertical-align: middle;">Transfer-I</td> |
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|
<td align="center" style="vertical-align: middle;">83.1%</td> |
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|
</tr> |
|
|
<tr> |
|
|
<td align="center" style="vertical-align: middle;">MS-ResNet18</td> |
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|
<td align="center" style="vertical-align: middle;">Transfer-II (Sim-to-Real)</td> |
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|
<td align="center" style="vertical-align: middle;"><strong>92.5%</strong></td> |
|
|
</tr> |
|
|
</table> |
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|
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|
*For full results and model weights, please visit our [GitHub Repo](https://github.com/Ruichen0424/I2E).* |
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|
|
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|
[](https://github.com/Ruichen0424/I2E) |
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|
[](https://huggingface.co/Ruichen0424/I2E) |
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## 📜 Citation |
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|
|
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|
If you find this work or the models useful, please cite our AAAI 2026 paper: |
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|
|
|
|
```bibtex |
|
|
@article{ma2025i2e, |
|
|
title={I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks}, |
|
|
author={Ma, Ruichen and Meng, Liwei and Qiao, Guanchao and Ning, Ning and Liu, Yang and Hu, Shaogang}, |
|
|
journal={arXiv preprint arXiv:2511.08065}, |
|
|
year={2025} |
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|
} |
|
|
``` |
