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N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras (Mini Train/Validation Splits)

Authors: Kim, Junho; Bae, Jaehyeok; Park, Gangin; Zhang, Dongsu; Kim, Young Min;

N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras (Mini Train/Validation Splits)

Abstract

This repository contains N-ImageNet along with its variants. N-ImageNet is a large-scale dataset for event-based object recognition that also allows robustness evaluation in various external conditions. The train split is separated into 10 parts. To extract the event data in each .zip file, run the following command: unzip train_Part_i.zip for f in $( ls train_Part_i ); do tar -xvf train_Part_i/$f -C train_Part_i/ ;done rm -rf train_Part_i/*.tar.gz After this, make a separate folder extracted_train and extracted_val as specified in https://github.com/82magnolia/n_imagenet#dataset-setup and start using mini N-ImageNet!

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Event Camera

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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