N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras

Benchmark Model Rank Results
classification-on-n-imagenetEvent Spike Tensor#1Accuracy (%): 48.93
classification-on-n-imagenetDiST#2Accuracy (%): 48.43
classification-on-n-imagenetSorted Time Surface#3Accuracy (%): 47.90
classification-on-n-imagenetEvent Histogram#4Accuracy (%): 47.73
classification-on-n-imagenetHATS#5Accuracy (%): 47.14
classification-on-n-imagenetBinary Event Image#6Accuracy (%): 46.36
classification-on-n-imagenetTimestamp Image#7Accuracy (%): 45.86
classification-on-n-imagenetEvent Image#8Accuracy (%): 45.77
classification-on-n-imagenetTime Surface#9Accuracy (%): 44.32
classification-on-n-imagenet-miniEvent Imge#1Accuracy (%): 61.42
classification-on-n-imagenet-miniEvent Histogram#2Accuracy (%): 61.02
classification-on-n-imagenet-miniTimestamp Image#3Accuracy (%): 60.46
classification-on-n-imagenet-miniDiST#4Accuracy (%): 59.74
classification-on-n-imagenet-miniSorted Time Surface#5Accuracy (%): 58.38
classification-on-n-imagenet-miniBinary Event Image#6Accuracy (%): 53.52