Image Classification
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/da-moon/classifiers-monorepo
https://github.com/dacostaHugo/Adversarial_attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/dailab/maxi-xai-lib
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/daimonae/Fundus-Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/daixiangzi/Grad_Cam-pytorch-resnet50
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/daixiangzi/ImprovedGan-pytorch
https://github.com/dalgu90/wrn-tensorflow
https://github.com/damedollaforthree/nrml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/danFromTelAviv/FReLU-Keras
FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks
https://github.com/danfenghong/Codes_SpectralFormer
SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers
https://github.com/danielamassiceti/CCA-visualdialogue
https://github.com/danielbis/python_cheap_fashion
https://github.com/danielchyeh/imagenet-100-pytorch
https://github.com/danielenricocahall/Keras-UNet
https://github.com/danielgordon10/vince
Self-Supervised Learning of Pretext-Invariant Representations
https://github.com/danielhavir/capsule-network
https://github.com/danielparada1/RL_SpaceInvaders
https://github.com/danielpatrickhug/Research_Paper_Parser
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/danielzgsilva/MOT
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/danilojodas/MobileNet
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/danstowell/insect_classifier_GDSC23_insecteffnet
https://github.com/dansuh17/alexnet-pytorch
https://github.com/dapeter/nas-for-kws
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/darkhill11/convolutional_GAN_basics
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/darkonhub/darkon
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/darkreapyre/HaaS
https://github.com/darkreapyre/HaaS-GitOps
https://github.com/darkreapyre/HaaS-KF
https://github.com/darkreapyre/HaaS-dev
https://github.com/darr/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/darrishabh/coviprox
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/darshans0200/YOLOTest
https://github.com/darya-baranovskaya/keyword_spotting
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/datalass1/fastai
https://github.com/datawiz1984/MLND_Capstone_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.