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/bryn-gnolbs/TAAF-for-Image-Classification
https://github.com/bshall/VectorQuantizedCPC
https://github.com/bshantam97/Attention_Based_Networks
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/btobab/R-Drop
https://github.com/buiquangmanhhp1999/age_gender_estimation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/bukson/kmnist-efcaps
https://github.com/buptdbj/darknet-windows-linux
https://github.com/burakalperen/Pytorch-Semantic-Segmentation
https://github.com/buriburisuri/timeseries_gan
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/butsugiri/shape
https://github.com/buyeah1109/KEN
https://github.com/buyeah1109/finc
https://github.com/bwconrad/masked-autoencoder
https://github.com/bwconrad/masked-distillation
https://github.com/bynchang/semi-supervised-VAE
https://github.com/bzhangGo/zero
Fixup Initialization: Residual Learning Without Normalization
https://github.com/c-bata/goptuna
Practical Bayesian Optimization of Machine Learning Algorithms
https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition
https://github.com/c1ph3rr/Very-Deep-Convolutional-Networks-For-Large-Scale-Image-Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/c1ph3rr/Wide-Residual-Networks
https://github.com/cachett/DCGANandCAE
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/caesarea38/doclangid
https://github.com/caffe-mocha/dcgan-pytorch-cifar10
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/caggursoy/Cross-Age-Effect
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/caggursoy/crossageeffect
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/caihaocong/Yolov3_AlexeyAB_Version
https://github.com/cailinhang/2018-Graduation-Project
https://github.com/cair/ICML-Massively-Parallel-and-Asynchronous-Tsetlin-Machine-Architecture
https://github.com/cair/PyTsetlinMachineCUDA
https://github.com/cair/TextUnderstandingTsetlinMachine
https://github.com/cair/TsetlinMachineC
https://github.com/cair/convolutional-tsetlin-machine-tutorial
https://github.com/cair/fast-tsetlin-machine-in-cuda-with-imdb-demo
https://github.com/cair/pyTsetlinMachineMT
https://github.com/cair/regression-tsetlin-machine
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.