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[논문정리] CoAtNet : Marrying Convolution and Attention for All Data Sizes
논문정보 CoAtNet : Marrying Convolution and Attention for All Data Sizes 논문정리 Abstract 트랜스포머로 인해 컴퓨터 비전에 대한 관심이 높아졌지만, SOTA 컨볼루션망에 비해서는 뒤쳐지고 있다. 이 연구에서는
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<CoatNet>
[딥러닝 논문리뷰] Class-Balanced Loss Based on Effective Number of Samples (CVPR 2019) (tistory.com)
[딥러닝 논문리뷰] Class-Balanced Loss Based on Effective Number of Samples (CVPR 2019)
CVPR 2019에 발표된 논문인 Class-Balanced Loss Based on Effective Number of Samples 를 정리한 글입니다. 데이터셋의 Class Imbalance를 해결하기 위해 새로운 Loss Design를 제안하는 논문입니다. Long Taile..
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세미나 - SKKU IIS LAB - CoAtNet: Marrying Convolution and Attention for All Data Sizes
세미나 - SKKU IIS LAB - CoAtNet: Marrying Convolution and Attention for All Data Sizes
iislab.skku.edu
==> ppt 있음
CoAtNet: Marrying Convolution and Attention for All Data Sizes - Paper Note (creamnuts.github.io)
CoAtNet: Marrying Convolution and Attention for All Data Sizes
Conv와 Attn을 합치는 방법을 제안 제안한 방법들의 적절성을 스스로 보임
creamnuts.github.io
==> 간단요약
https://www.youtube.com/watch?v=VoRQiKQcdcI&pp=ugMICgJrbxABGAE%3D
https://www.youtube.com/watch?v=lZdyER5nOXU&pp=ugMICgJrbxABGAE%3D
[1] CoAtNet
CoAtNet : Marrying Convolution and Attention for All Data Sizes Abstract Computer Vision에서 Transformers의 관심은 커지고 있지만, SOTA convolutional networks 보다 뒤쳐져있다. Transformers는 larger m..
lastwinter.tistory.com
Google AI Introduces Two New Families of Neural Networks Called ‘EfficientNetV2’ and ‘CoAtNet’ For Image Recognition
Posted in r/artificial by u/techsucker • 54 points and 1 comment
www.reddit.com
==> code
6) CoAtNet(Convolution+Transformer) - 한땀한땀 딥러닝 컴퓨터 비전 백과사전 (wikidocs.net)
6) CoAtNet(Convolution+Transformer)
## Background 2021년 6월, Google AI 팀에서 EfficientNet-V2와 함께 발표한 CoatNet은 발표와 동시에 ImageNet top 1 ac ...
wikidocs.net
==> wikidocs
CoAtNet: Marrying Convolution and Attention for All Data Sizes (milkclouds.work)
CoAtNet: Marrying Convolution and Attention for All Data Sizes
요약 이 논문에서는 convolution과 relative attention(의 변형)를 적절히 섞고 JFT에서 pre-training해 ImageNet-1K에서 90.88%로 SOTA를 차지하고 있는 CoAtNet(코트넷)에 대해 소개한다. 2. Model 2.1 Merging Convolution and S
milkclouds.work
CoAtNet: Marrying Convolution and Attention for All Data Sizes | Papers With Code
==> code
CoAtNet: Marrying Convolution and Attention for All Data Sizes (tistory.com)
CoAtNet: Marrying Convolution and Attention for All Data Sizes
Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan Google Research, Brain Team (2021.06) Abstract Transformer 아직 vision task에서 SOTA convolutional network보다 성능이 떨어짐 Transformer는 더 큰 cap..
dlaiml.tistory.com
<code>
GitHub - chinhsuanwu/coatnet-pytorch: A PyTorch implementation of "CoAtNet: Marrying Convolution and Attention for All Data Size
A PyTorch implementation of "CoAtNet: Marrying Convolution and Attention for All Data Sizes". - GitHub - chinhsuanwu/coatnet-pytorch: A PyTorch implementation of "CoAtNet: Marrying C...
github.com
==> CoAtNet 모델 구조 (pytorch)
GitHub - tyeso/Image_Classification_with_CoAtNet_and_ResNet18
Contribute to tyeso/Image_Classification_with_CoAtNet_and_ResNet18 development by creating an account on GitHub.
github.com
==> 사진으로 훈련하는 코드
GitHub - leondgarse/keras_cv_attention_models: Keras/Tensorflow attention models including beit,botnet,CMT,CoaT,CoAtNet,convnext
Keras/Tensorflow attention models including beit,botnet,CMT,CoaT,CoAtNet,convnext,cotnet,davit,efficientdet,efficientnet,fbnet,gmlp,halonet,lcnet,levit,mlp-mixer,mobilevit,nfnets,regnet,resmlp,resn...
github.com
keras 모델인데 훈련 하는 것까지 보여주긴함
코드가 잇는지는 모르겟음
훈련한 모델을 주기는 함
CoAtNet-PyTorch/CoAtNet.py at main · LongLeCE/CoAtNet-PyTorch · GitHub
GitHub - LongLeCE/CoAtNet-PyTorch: A PyTorch implementation of https://arxiv.org/pdf/2106.04803v2.pdf
A PyTorch implementation of https://arxiv.org/pdf/2106.04803v2.pdf - GitHub - LongLeCE/CoAtNet-PyTorch: A PyTorch implementation of https://arxiv.org/pdf/2106.04803v2.pdf
github.com
이것도 코드가 모델 구조별로 있긴 한데 eval 코드만 있음
CoAtNet/Cifar10有验证集.ipynb at master · 729593736/CoAtNet · GitHub
GitHub - 729593736/CoAtNet: This is a classification task based on CIFAR10，Accuracy is about 87%（without pre-training），T
This is a classification task based on CIFAR10，Accuracy is about 87%（without pre-training），The net is CoAtNet（0-5，total coatnet family），with the confusion matrix and acc&loss visualization - Gi...
github.com
ㅅㅂ train코드가 있긴 한데 짱깨언어임 짜증남
cifar10을 가지고 coatnet훈련한 코드.....
진짜 ㅈ같을때보자!
GitHub - salwaar/Malware_Classification: Malware Classification with CoAtNet: Marrying Convolution and Attention for Visual Imag
Malware Classification with CoAtNet: Marrying Convolution and Attention for Visual Images pytorch - GitHub - salwaar/Malware_Classification: Malware Classification with CoAtNet: Marrying Convolutio...
github.com
이거도 train코드 있고 모델변형할 수 있음