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diff --git a/Load_Datasets_Example.ipynb b/Load_Datasets_Example.ipynb
new file mode 100644
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--- /dev/null
+++ b/Load_Datasets_Example.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "from torchvision import datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_path = '/ssd/Datasets/I2E-ImageNet/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class I2E_NpzFolder(datasets.DatasetFolder):\n",
+    "    def __init__(self, root, loader=None, extensions=['npz'], transform=None, target_transform=None, is_valid_file=None, allow_empty=False):\n",
+    "        super(I2E_NpzFolder, self).__init__(root, loader, extensions, transform, target_transform, is_valid_file, allow_empty)\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        path, target = self.samples[index]\n",
+    "        sample = torch.from_numpy(np.load(path)['arr_0']).float()\n",
+    "        if self.transform is not None:\n",
+    "            sample = self.transform(sample)\n",
+    "        if self.target_transform is not None:\n",
+    "            target = self.target_transform(target)\n",
+    "\n",
+    "        return sample, target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "len(train_dataset): 1281167, len(val_dataset): 50000\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_dataset = I2E_NpzFolder(root=os.path.join(dataset_path, 'train'))\n",
+    "val_dataset = I2E_NpzFolder(root=os.path.join(dataset_path, 'val'))\n",
+    "print(f'len(train_dataset): {len(train_dataset)}, len(val_dataset): {len(val_dataset)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "img.shape: torch.Size([8, 2, 224, 224]), label: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "img, label = train_dataset[0]\n",
+    "print(f'img.shape: {img.shape}, label: {label}')        # [T=8, p=2, H, W]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pytorch291",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/README.md b/README.md
index 3a5cec29fcf9216a998e343e59e59f5fc093bd9f..a3e0322016aba4f3c5e4311f1fdede401fe3ab5f 100644
--- a/README.md
+++ b/README.md
@@ -1,1805 +1,7 @@
 ---
-dataset_info:
-- config_name: I2E-CIFAR10
-  features:
-  - name: file_path
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': '0'
-          '1': '1'
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-          '9': '9'
-  - name: data
-    dtype: binary
-  splits:
-  - name: train
-    num_bytes: 1646538890
-    num_examples: 50000
-  - name: validation
-    num_bytes: 329298890
-    num_examples: 10000
-  download_size: 464478602
-  dataset_size: 1975837780
-- config_name: I2E-CIFAR100
-  features:
-  - name: file_path
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': '0'
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-  - name: data
-    dtype: binary
-  splits:
-  - name: train
-    num_bytes: 1646583890
-    num_examples: 50000
-  - name: validation
-    num_bytes: 329307890
-    num_examples: 10000
-  download_size: 462298257
-  dataset_size: 1975891780
-- config_name: I2E-Caltech101
-  features:
-  - name: file_path
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': Faces
-          '1': Faces_easy
-          '2': Leopards
-          '3': Motorbikes
-          '4': accordion
-          '5': airplanes
-          '6': anchor
-          '7': ant
-          '8': barrel
-          '9': bass
-          '10': beaver
-          '11': binocular
-          '12': bonsai
-          '13': brain
-          '14': brontosaurus
-          '15': buddha
-          '16': butterfly
-          '17': camera
-          '18': cannon
-          '19': car_side
-          '20': ceiling_fan
-          '21': cellphone
-          '22': chair
-          '23': chandelier
-          '24': cougar_body
-          '25': cougar_face
-          '26': crab
-          '27': crayfish
-          '28': crocodile
-          '29': crocodile_head
-          '30': cup
-          '31': dalmatian
-          '32': dollar_bill
-          '33': dolphin
-          '34': dragonfly
-          '35': electric_guitar
-          '36': elephant
-          '37': emu
-          '38': euphonium
-          '39': ewer
-          '40': ferry
-          '41': flamingo
-          '42': flamingo_head
-          '43': garfield
-          '44': gerenuk
-          '45': gramophone
-          '46': grand_piano
-          '47': hawksbill
-          '48': headphone
-          '49': hedgehog
-          '50': helicopter
-          '51': ibis
-          '52': inline_skate
-          '53': joshua_tree
-          '54': kangaroo
-          '55': ketch
-          '56': lamp
-          '57': laptop
-          '58': llama
-          '59': lobster
-          '60': lotus
-          '61': mandolin
-          '62': mayfly
-          '63': menorah
-          '64': metronome
-          '65': minaret
-          '66': nautilus
-          '67': octopus
-          '68': okapi
-          '69': pagoda
-          '70': panda
-          '71': pigeon
-          '72': pizza
-          '73': platypus
-          '74': pyramid
-          '75': revolver
-          '76': rhino
-          '77': rooster
-          '78': saxophone
-          '79': schooner
-          '80': scissors
-          '81': scorpion
-          '82': sea_horse
-          '83': snoopy
-          '84': soccer_ball
-          '85': stapler
-          '86': starfish
-          '87': stegosaurus
-          '88': stop_sign
-          '89': strawberry
-          '90': sunflower
-          '91': tick
-          '92': trilobite
-          '93': umbrella
-          '94': watch
-          '95': water_lilly
-          '96': wheelchair
-          '97': wild_cat
-          '98': windsor_chair
-          '99': wrench
-          '100': yin_yang
-  - name: data
-    dtype: binary
-  splits:
-  - name: train
-    num_bytes: 872272607
-    num_examples: 8677
-  download_size: 344357976
-  dataset_size: 872272607
-- config_name: I2E-Caltech256
-  features:
-  - name: file_path
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': 001.ak47
-          '1': 002.american-flag
-          '2': 003.backpack
-          '3': 004.baseball-bat
-          '4': 005.baseball-glove
-          '5': 006.basketball-hoop
-          '6': 007.bat
-          '7': 008.bathtub
-          '8': 009.bear
-          '9': 010.beer-mug
-          '10': 011.billiards
-          '11': 012.binoculars
-          '12': 013.birdbath
-          '13': 014.blimp
-          '14': 015.bonsai-101
-          '15': 016.boom-box
-          '16': 017.bowling-ball
-          '17': 018.bowling-pin
-          '18': 019.boxing-glove
-          '19': 020.brain-101
-          '20': 021.breadmaker
-          '21': 022.buddha-101
-          '22': 023.bulldozer
-          '23': 024.butterfly
-          '24': 025.cactus
-          '25': 026.cake
-          '26': 027.calculator
-          '27': 028.camel
-          '28': 029.cannon
-          '29': 030.canoe
-          '30': 031.car-tire
-          '31': 032.cartman
-          '32': 033.cd
-          '33': 034.centipede
-          '34': 035.cereal-box
-          '35': 036.chandelier-101
-          '36': 037.chess-board
-          '37': 038.chimp
-          '38': 039.chopsticks
-          '39': 040.cockroach
-          '40': 041.coffee-mug
-          '41': 042.coffin
-          '42': 043.coin
-          '43': 044.comet
-          '44': 045.computer-keyboard
-          '45': 046.computer-monitor
-          '46': 047.computer-mouse
-          '47': 048.conch
-          '48': 049.cormorant
-          '49': 050.covered-wagon
-          '50': 051.cowboy-hat
-          '51': 052.crab-101
-          '52': 053.desk-globe
-          '53': 054.diamond-ring
-          '54': 055.dice
-          '55': 056.dog
-          '56': 057.dolphin-101
-          '57': 058.doorknob
-          '58': 059.drinking-straw
-          '59': 060.duck
-          '60': 061.dumb-bell
-          '61': 062.eiffel-tower
-          '62': 063.electric-guitar-101
-          '63': 064.elephant-101
-          '64': 065.elk
-          '65': 066.ewer-101
-          '66': 067.eyeglasses
-          '67': 068.fern
-          '68': 069.fighter-jet
-          '69': 070.fire-extinguisher
-          '70': 071.fire-hydrant
-          '71': 072.fire-truck
-          '72': 073.fireworks
-          '73': 074.flashlight
-          '74': 075.floppy-disk
-          '75': 076.football-helmet
-          '76': 077.french-horn
-          '77': 078.fried-egg
-          '78': 079.frisbee
-          '79': 080.frog
-          '80': 081.frying-pan
-          '81': 082.galaxy
-          '82': 083.gas-pump
-          '83': 084.giraffe
-          '84': 085.goat
-          '85': 086.golden-gate-bridge
-          '86': 087.goldfish
-          '87': 088.golf-ball
-          '88': 089.goose
-          '89': 090.gorilla
-          '90': 091.grand-piano-101
-          '91': 092.grapes
-          '92': 093.grasshopper
-          '93': 094.guitar-pick
-          '94': 095.hamburger
-          '95': 096.hammock
-          '96': 097.harmonica
-          '97': 098.harp
-          '98': 099.harpsichord
-          '99': 100.hawksbill-101
-          '100': 101.head-phones
-          '101': 102.helicopter-101
-          '102': 103.hibiscus
-          '103': 104.homer-simpson
-          '104': 105.horse
-          '105': 106.horseshoe-crab
-          '106': 107.hot-air-balloon
-          '107': 108.hot-dog
-          '108': 109.hot-tub
-          '109': 110.hourglass
-          '110': 111.house-fly
-          '111': 112.human-skeleton
-          '112': 113.hummingbird
-          '113': 114.ibis-101
-          '114': 115.ice-cream-cone
-          '115': 116.iguana
-          '116': 117.ipod
-          '117': 118.iris
-          '118': 119.jesus-christ
-          '119': 120.joy-stick
-          '120': 121.kangaroo-101
-          '121': 122.kayak
-          '122': 123.ketch-101
-          '123': 124.killer-whale
-          '124': 125.knife
-          '125': 126.ladder
-          '126': 127.laptop-101
-          '127': 128.lathe
-          '128': 129.leopards-101
-          '129': 130.license-plate
-          '130': 131.lightbulb
-          '131': 132.light-house
-          '132': 133.lightning
-          '133': 134.llama-101
-          '134': 135.mailbox
-          '135': 136.mandolin
-          '136': 137.mars
-          '137': 138.mattress
-          '138': 139.megaphone
-          '139': 140.menorah-101
-          '140': 141.microscope
-          '141': 142.microwave
-          '142': 143.minaret
-          '143': 144.minotaur
-          '144': 145.motorbikes-101
-          '145': 146.mountain-bike
-          '146': 147.mushroom
-          '147': 148.mussels
-          '148': 149.necktie
-          '149': 150.octopus
-          '150': 151.ostrich
-          '151': 152.owl
-          '152': 153.palm-pilot
-          '153': 154.palm-tree
-          '154': 155.paperclip
-          '155': 156.paper-shredder
-          '156': 157.pci-card
-          '157': 158.penguin
-          '158': 159.people
-          '159': 160.pez-dispenser
-          '160': 161.photocopier
-          '161': 162.picnic-table
-          '162': 163.playing-card
-          '163': 164.porcupine
-          '164': 165.pram
-          '165': 166.praying-mantis
-          '166': 167.pyramid
-          '167': 168.raccoon
-          '168': 169.radio-telescope
-          '169': 170.rainbow
-          '170': 171.refrigerator
-          '171': 172.revolver-101
-          '172': 173.rifle
-          '173': 174.rotary-phone
-          '174': 175.roulette-wheel
-          '175': 176.saddle
-          '176': 177.saturn
-          '177': 178.school-bus
-          '178': 179.scorpion-101
-          '179': 180.screwdriver
-          '180': 181.segway
-          '181': 182.self-propelled-lawn-mower
-          '182': 183.sextant
-          '183': 184.sheet-music
-          '184': 185.skateboard
-          '185': 186.skunk
-          '186': 187.skyscraper
-          '187': 188.smokestack
-          '188': 189.snail
-          '189': 190.snake
-          '190': 191.sneaker
-          '191': 192.snowmobile
-          '192': 193.soccer-ball
-          '193': 194.socks
-          '194': 195.soda-can
-          '195': 196.spaghetti
-          '196': 197.speed-boat
-          '197': 198.spider
-          '198': 199.spoon
-          '199': 200.stained-glass
-          '200': 201.starfish-101
-          '201': 202.steering-wheel
-          '202': 203.stirrups
-          '203': 204.sunflower-101
-          '204': 205.superman
-          '205': 206.sushi
-          '206': 207.swan
-          '207': 208.swiss-army-knife
-          '208': 209.sword
-          '209': 210.syringe
-          '210': 211.tambourine
-          '211': 212.teapot
-          '212': 213.teddy-bear
-          '213': 214.teepee
-          '214': 215.telephone-box
-          '215': 216.tennis-ball
-          '216': 217.tennis-court
-          '217': 218.tennis-racket
-          '218': 219.theodolite
-          '219': 220.toaster
-          '220': 221.tomato
-          '221': 222.tombstone
-          '222': 223.top-hat
-          '223': 224.touring-bike
-          '224': 225.tower-pisa
-          '225': 226.traffic-light
-          '226': 227.treadmill
-          '227': 228.triceratops
-          '228': 229.tricycle
-          '229': 230.trilobite-101
-          '230': 231.tripod
-          '231': 232.t-shirt
-          '232': 233.tuning-fork
-          '233': 234.tweezer
-          '234': 235.umbrella-101
-          '235': 236.unicorn
-          '236': 237.vcr
-          '237': 238.video-projector
-          '238': 239.washing-machine
-          '239': 240.watch-101
-          '240': 241.waterfall
-          '241': 242.watermelon
-          '242': 243.welding-mask
-          '243': 244.wheelbarrow
-          '244': 245.windmill
-          '245': 246.wine-bottle
-          '246': 247.xylophone
-          '247': 248.yarmulke
-          '248': 249.yo-yo
-          '249': 250.zebra
-          '250': 251.airplanes-101
-          '251': 252.car-side-101
-          '252': 253.faces-easy-101
-          '253': 254.greyhound
-          '254': 255.tennis-shoes
-          '255': 256.toad
-          '256': 257.clutter
-  - name: data
-    dtype: binary
-  splits:
-  - name: train
-    num_bytes: 3076928106
-    num_examples: 30607
-  download_size: 1165568633
-  dataset_size: 3076928106
-- config_name: I2E-FashionMNIST
-  features:
-  - name: file_path
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': '0'
-          '1': '1'
-          '2': '2'
-          '3': '3'
-          '4': '4'
-          '5': '5'
-          '6': '6'
-          '7': '7'
-          '8': '8'
-          '9': '9'
-  - name: data
-    dtype: binary
-  splits:
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-    num_bytes: 132648890
-    num_examples: 60000
-  - name: validation
-    num_bytes: 22098890
-    num_examples: 10000
-  download_size: 68196022
-  dataset_size: 154747780
-- config_name: I2E-ImageNet
-  features:
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-  - name: data
-    dtype: binary
-  splits:
-  - name: train
-    num_bytes: 128798692994
-    num_examples: 1281167
-  - name: validation
-    num_bytes: 5027050000
-    num_examples: 50000
-  download_size: 57961329620
-  dataset_size: 133825742994
-- config_name: I2E-MNIST
-  features:
-  - name: file_path
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': '0'
-          '1': '1'
-          '2': '2'
-          '3': '3'
-          '4': '4'
-          '5': '5'
-          '6': '6'
-          '7': '7'
-          '8': '8'
-          '9': '9'
-  - name: data
-    dtype: binary
-  splits:
-  - 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:
-  - name: file_path
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': n01532829
-          '1': n01558993
-          '2': n01704323
-          '3': n01749939
-          '4': n01770081
-          '5': n01843383
-          '6': n01855672
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-          '8': n01930112
-          '9': n01981276
-          '10': n02074367
-          '11': n02089867
-          '12': n02091244
-          '13': n02091831
-          '14': n02099601
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-          '19': n02108915
-          '20': n02110063
-          '21': n02110341
-          '22': n02111277
-          '23': n02113712
-          '24': n02114548
-          '25': n02116738
-          '26': n02120079
-          '27': n02129165
-          '28': n02138441
-          '29': n02165456
-          '30': n02174001
-          '31': n02219486
-          '32': n02443484
-          '33': n02457408
-          '34': n02606052
-          '35': n02687172
-          '36': n02747177
-          '37': n02795169
-          '38': n02823428
-          '39': n02871525
-          '40': n02950826
-          '41': n02966193
-          '42': n02971356
-          '43': n02981792
-          '44': n03017168
-          '45': n03047690
-          '46': n03062245
-          '47': n03075370
-          '48': n03127925
-          '49': n03146219
-          '50': n03207743
-          '51': n03220513
-          '52': n03272010
-          '53': n03337140
-          '54': n03347037
-          '55': n03400231
-          '56': n03417042
-          '57': n03476684
-          '58': n03527444
-          '59': n03535780
-          '60': n03544143
-          '61': n03584254
-          '62': n03676483
-          '63': n03770439
-          '64': n03773504
-          '65': n03775546
-          '66': n03838899
-          '67': n03854065
-          '68': n03888605
-          '69': n03908618
-          '70': n03924679
-          '71': n03980874
-          '72': n03998194
-          '73': n04067472
-          '74': n04146614
-          '75': n04149813
-          '76': n04243546
-          '77': n04251144
-          '78': n04258138
-          '79': n04275548
-          '80': n04296562
-          '81': n04389033
-          '82': n04418357
-          '83': n04435653
-          '84': n04443257
-          '85': n04509417
-          '86': n04515003
-          '87': n04522168
-          '88': n04596742
-          '89': n04604644
-          '90': n04612504
-          '91': n06794110
-          '92': n07584110
-          '93': n07613480
-          '94': n07697537
-          '95': n07747607
-          '96': n09246464
-          '97': n09256479
-          '98': n13054560
-          '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>
@@ -1811,174 +13,28 @@ language:
 
 [![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Paper-FFD21E?style=flat-square&logo=huggingface&logoColor=black)](https://huggingface.co/papers/2511.08065)
 [![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Models-FFD21E?style=flat-square&logo=huggingface&logoColor=black)](https://huggingface.co/Ruichen0424/I2E)
-
-[![YouTube](https://img.shields.io/badge/YouTube-Video-FF0000?style=flat-square&logo=youtube&logoColor=white)](https://youtu.be/v9z0pn8kTsI?si=dkbjR6AV_RgCU3wA)
-[![Bilibili](https://img.shields.io/badge/Bilibili-Video-FE7398?style=flat-square&logo=bilibili&logoColor=white)](https://www.bilibili.com/video/BV14XrfBjENb/)
 </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 |
+This repository contains the **I2E-Datasets** for the paper **"I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks"**, which has been accepted for **Oral Presentation at AAAI 2026**.
 
-> 🔜 **Coming Soon:** Object Detection and Semantic Segmentation datasets.
+**I2E** is a pioneering framework that bridges the data scarcity gap in neuromorphic computing. 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).
 
-## 🛠️ Preprocessing Protocol
+### ✨ Key Highlights
+* **SOTA Performance**: Achieves **60.50%** top-1 accuracy on Event-based ImageNet.
+* **Sim-to-Real Transfer**: Pre-training on I2E data enables **92.5%** accuracy on real-world CIFAR10-DVS, setting a new benchmark.
+* **Real-Time Conversion**: Enables on-the-fly data augmentation for deep SNN training.
 
-To ensure reproducibility, we specify the exact data augmentation pipeline applied to the static images **before** I2E conversion. 
+## 🏆 Model Zoo & Results
 
-The `(H, W)` in the code below corresponds to the "Resolution" column in the Dataset Catalog above.
+We provide pre-trained models for **I2E-CIFAR** and **I2E-ImageNet**. You can download the `.pth` files directly from the [**Files and versions**](https://huggingface.co/Ruichen0424/I2E/tree/main) tab in model repository.
 
-```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)
-
-    events, labels = next(iter(train_loader))
-    print(f"✅ Loaded Batch Shape: {events.shape}") # Expect: [32, T, 2, H, W]
-    print(f"✅ Labels: {labels}")
-```
-
-## 🏆 Results (SOTA)
-
-Our I2E-pretraining sets new benchmarks for Sim-to-Real transfer on **CIFAR10-DVS**.
+[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Models-FFD21E?style=flat-square&logo=huggingface&logoColor=black)](https://huggingface.co/Ruichen0424/I2E)
 
 <table border="1">
     <tr>
-        <th>Dataset</th>
+        <th>Target Dataset</th>
         <th align="center">Architecture</th>
         <th align="center">Method</th>
         <th align="center">Top-1 Acc</th>
@@ -2000,12 +56,110 @@ Our I2E-pretraining sets new benchmarks for Sim-to-Real transfer on **CIFAR10-DV
         <td align="center" style="vertical-align: middle;">Transfer-II (Sim-to-Real)</td>
         <td align="center" style="vertical-align: middle;"><strong>92.5%</strong></td>
     </tr>
+    <!-- I2E-CIFAR10 -->
+    <tr>
+        <td rowspan="3" align="center" style="vertical-align: middle;"><strong>I2E-CIFAR10</strong></td>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Baseline-I</td>
+        <td align="center" style="vertical-align: middle;">85.07%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Baseline-II</td>
+        <td align="center" style="vertical-align: middle;">89.23%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Transfer-I</td>
+        <td align="center" style="vertical-align: middle;"><strong>90.86%</strong></td>
+    </tr>
+    <!-- I2E-CIFAR100 -->
+    <tr>
+        <td rowspan="3" align="center" style="vertical-align: middle;"><strong>I2E-CIFAR100</strong></td>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Baseline-I</td>
+        <td align="center" style="vertical-align: middle;">51.32%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Baseline-II</td>
+        <td align="center" style="vertical-align: middle;">60.68%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Transfer-I</td>
+        <td align="center" style="vertical-align: middle;"><strong>64.53%</strong></td>
+    </tr>
+    <!-- I2E-ImageNet -->
+    <tr>
+        <td rowspan="4" align="center" style="vertical-align: middle;"><strong>I2E-ImageNet</strong></td>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Baseline-I</td>
+        <td align="center" style="vertical-align: middle;">48.30%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Baseline-II</td>
+        <td align="center" style="vertical-align: middle;">57.97%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Transfer-I</td>
+        <td align="center" style="vertical-align: middle;">59.28%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet34</td>
+        <td align="center" style="vertical-align: middle;">Baseline-II</td>
+        <td align="center" style="vertical-align: middle;"><strong>60.50%</strong></td>
+    </tr>
 </table>
 
-*For full results and model weights, please visit our [GitHub Repo](https://github.com/Ruichen0424/I2E).*
+> **Method Legend:**
+> * **Baseline-I**: Training from scratch with minimal augmentation.
+> * **Baseline-II**: Training from scratch with full augmentation.
+> * **Transfer-I**: Fine-tuning from Static ImageNet (or I2E-ImageNet for CIFAR targets).
+> * **Transfer-II**: Fine-tuning from I2E-CIFAR10.
+
+## 👁️ Visualization
+
+Below is the visualization of the I2E conversion process. We illustrate the high-fidelity conversion from static RGB images to dynamic event streams.
+
+More than 200 additional visualization comparisons can be found in [Visualization.md](./Visualization.md).
+
+<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>
+
+## 💻 Usage
+The generated I2E-Datasets are provided in the [**Files and versions**](https://huggingface.co/datasets/UESTC-BICS/I2E/tree/main) section, including I2E-CIFAR10, I2E-CIFAR100, and I2E-ImageNet.
+For I2E-ImageNet, the following command can be used to concatenate the zip file.
+``` bash
+cat ./I2E-ImageNet_split.part_* > ./I2E-ImageNet.zip
+```
+We also provide MD5 checksums to facilitate verification. Use the following command to verify the compressed files:
+``` bash
+md5sum -c md5.txt
+```
+
+This repository hosts the **datasets only**.
+
+For the **I2E dataset generation code**, **training scripts**, and detailed usage instructions, please refer to our official GitHub repository.
+
+To generate the datasets (I2E-CIFAR10, I2E-CIFAR100, I2E-ImageNet) yourself using the I2E algorithm, please follow the instructions in the GitHub README.
 
 [![GitHub](https://img.shields.io/badge/GitHub-Repository-black?logo=github)](https://github.com/Ruichen0424/I2E)
-[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Models-FFD21E?style=flat-square&logo=huggingface&logoColor=black)](https://huggingface.co/Ruichen0424/I2E)
+
 
 ## 📜 Citation
 
@@ -2018,7 +172,4 @@ If you find this work or the models useful, please cite our AAAI 2026 paper:
   journal={arXiv preprint arXiv:2511.08065},
   year={2025}
 }
-```
-
-## 🖼️ Poster
-![poster](./assets/poster.png)
\ No newline at end of file
+```
\ No newline at end of file
diff --git a/assets/converted_1.gif b/assets/converted_1.gif
index a02d6bc8d84ca6d683c5f128b034e80b55584902..6f8afa248c95a79af322f90abb4d25a32503968d 100644
--- a/assets/converted_1.gif
+++ b/assets/converted_1.gif
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a8bca7fe674daf234f67b60d56040ebea7965a55a827fb562265739f8738a600
-size 36155
+oid sha256:01ed2b143e2824d764261ffe26df0ad51770733536300059b6e829b5647256dd
+size 35925
diff --git a/assets/converted_2.gif b/assets/converted_2.gif
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-{
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-        "num_bytes": 1646538890,
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-        "num_examples": 10000
-      }
-    ],
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-      }
-    ],
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-    "dataset_size": 1975891780
-  },
-  "I2E-Caltech101": {
-    "features": [
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-        "dtype": "string"
-      },
-      {
-        "name": "label",
-        "dtype": {
-          "class_label": {
-            "names": {
-              "0": "Faces",
-              "1": "Faces_easy",
-              "2": "Leopards",
-              "3": "Motorbikes",
-              "4": "accordion",
-              "5": "airplanes",
-              "6": "anchor",
-              "7": "ant",
-              "8": "barrel",
-              "9": "bass",
-              "10": "beaver",
-              "11": "binocular",
-              "12": "bonsai",
-              "13": "brain",
-              "14": "brontosaurus",
-              "15": "buddha",
-              "16": "butterfly",
-              "17": "camera",
-              "18": "cannon",
-              "19": "car_side",
-              "20": "ceiling_fan",
-              "21": "cellphone",
-              "22": "chair",
-              "23": "chandelier",
-              "24": "cougar_body",
-              "25": "cougar_face",
-              "26": "crab",
-              "27": "crayfish",
-              "28": "crocodile",
-              "29": "crocodile_head",
-              "30": "cup",
-              "31": "dalmatian",
-              "32": "dollar_bill",
-              "33": "dolphin",
-              "34": "dragonfly",
-              "35": "electric_guitar",
-              "36": "elephant",
-              "37": "emu",
-              "38": "euphonium",
-              "39": "ewer",
-              "40": "ferry",
-              "41": "flamingo",
-              "42": "flamingo_head",
-              "43": "garfield",
-              "44": "gerenuk",
-              "45": "gramophone",
-              "46": "grand_piano",
-              "47": "hawksbill",
-              "48": "headphone",
-              "49": "hedgehog",
-              "50": "helicopter",
-              "51": "ibis",
-              "52": "inline_skate",
-              "53": "joshua_tree",
-              "54": "kangaroo",
-              "55": "ketch",
-              "56": "lamp",
-              "57": "laptop",
-              "58": "llama",
-              "59": "lobster",
-              "60": "lotus",
-              "61": "mandolin",
-              "62": "mayfly",
-              "63": "menorah",
-              "64": "metronome",
-              "65": "minaret",
-              "66": "nautilus",
-              "67": "octopus",
-              "68": "okapi",
-              "69": "pagoda",
-              "70": "panda",
-              "71": "pigeon",
-              "72": "pizza",
-              "73": "platypus",
-              "74": "pyramid",
-              "75": "revolver",
-              "76": "rhino",
-              "77": "rooster",
-              "78": "saxophone",
-              "79": "schooner",
-              "80": "scissors",
-              "81": "scorpion",
-              "82": "sea_horse",
-              "83": "snoopy",
-              "84": "soccer_ball",
-              "85": "stapler",
-              "86": "starfish",
-              "87": "stegosaurus",
-              "88": "stop_sign",
-              "89": "strawberry",
-              "90": "sunflower",
-              "91": "tick",
-              "92": "trilobite",
-              "93": "umbrella",
-              "94": "watch",
-              "95": "water_lilly",
-              "96": "wheelchair",
-              "97": "wild_cat",
-              "98": "windsor_chair",
-              "99": "wrench",
-              "100": "yin_yang"
-            }
-          }
-        }
-      },
-      {
-        "name": "data",
-        "dtype": "binary"
-      }
-    ],
-    "splits": [
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-        "num_examples": 8677
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-    ],
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-  },
-  "I2E-Caltech256": {
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-      },
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-        "dtype": {
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-              "5": "006.basketball-hoop",
-              "6": "007.bat",
-              "7": "008.bathtub",
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-              "10": "011.billiards",
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-              "14": "015.bonsai-101",
-              "15": "016.boom-box",
-              "16": "017.bowling-ball",
-              "17": "018.bowling-pin",
-              "18": "019.boxing-glove",
-              "19": "020.brain-101",
-              "20": "021.breadmaker",
-              "21": "022.buddha-101",
-              "22": "023.bulldozer",
-              "23": "024.butterfly",
-              "24": "025.cactus",
-              "25": "026.cake",
-              "26": "027.calculator",
-              "27": "028.camel",
-              "28": "029.cannon",
-              "29": "030.canoe",
-              "30": "031.car-tire",
-              "31": "032.cartman",
-              "32": "033.cd",
-              "33": "034.centipede",
-              "34": "035.cereal-box",
-              "35": "036.chandelier-101",
-              "36": "037.chess-board",
-              "37": "038.chimp",
-              "38": "039.chopsticks",
-              "39": "040.cockroach",
-              "40": "041.coffee-mug",
-              "41": "042.coffin",
-              "42": "043.coin",
-              "43": "044.comet",
-              "44": "045.computer-keyboard",
-              "45": "046.computer-monitor",
-              "46": "047.computer-mouse",
-              "47": "048.conch",
-              "48": "049.cormorant",
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-              "50": "051.cowboy-hat",
-              "51": "052.crab-101",
-              "52": "053.desk-globe",
-              "53": "054.diamond-ring",
-              "54": "055.dice",
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-              "56": "057.dolphin-101",
-              "57": "058.doorknob",
-              "58": "059.drinking-straw",
-              "59": "060.duck",
-              "60": "061.dumb-bell",
-              "61": "062.eiffel-tower",
-              "62": "063.electric-guitar-101",
-              "63": "064.elephant-101",
-              "64": "065.elk",
-              "65": "066.ewer-101",
-              "66": "067.eyeglasses",
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-              "91": "092.grapes",
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-              "93": "094.guitar-pick",
-              "94": "095.hamburger",
-              "95": "096.hammock",
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-              "99": "100.hawksbill-101",
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-              "101": "102.helicopter-101",
-              "102": "103.hibiscus",
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-              "90": "n04612504",
-              "91": "n06794110",
-              "92": "n07584110",
-              "93": "n07613480",
-              "94": "n07697537",
-              "95": "n07747607",
-              "96": "n09246464",
-              "97": "n09256479",
-              "98": "n13054560",
-              "99": "n13133613"
-            }
-          }
-        }
-      },
-      {
-        "name": "data",
-        "dtype": "binary"
-      }
-    ],
-    "splits": [
-      {
-        "name": "train",
-        "num_bytes": 6031941884,
-        "num_examples": 60000
-      }
-    ],
-    "download_size": 2568434568,
-    "dataset_size": 6031941884
-  }
-}
\ No newline at end of file
diff --git a/load_i2e_datasets.py b/load_i2e_datasets.py
deleted file mode 100644
index d0694f6c044c4247b7ab0fb0249634512acb9124..0000000000000000000000000000000000000000
--- a/load_i2e_datasets.py
+++ /dev/null
@@ -1,75 +0,0 @@
-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)
-
-    events, labels = next(iter(train_loader))
-    print(f"✅ Loaded Batch Shape: {events.shape}") # Expect: [32, T, 2, H, W]
-    print(f"✅ Labels: {labels}")
\ No newline at end of file