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/hlzhang109/ddg
https://github.com/hmichaeli/alias_free_convnets
https://github.com/hmohebbi/TF-Adapter-BERT
https://github.com/hn410/exploring-weight-balancing-on-long-tailed-recognition-problem
https://github.com/hoanghuy89/CNN-from-first-principle
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/hoangthang1607/nfnets-Tensorflow-2
High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/hoidn/ptychopinn
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/holdfire/CLS
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/holdfire/FAS
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/holgerbovbjerg/data2vec-kws
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/hollance/VGGNet-Metal
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/holstgr-kaust/keras-tutorials
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/holyseven/PSPNet-TF-Reproduce
https://github.com/holytrick/AlexeyAB_darknet
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/hongkunsun/paratranscnn
https://github.com/hongxin001/logitnorm_ood
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
https://github.com/hongxin001/open-sampling
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
https://github.com/hongyi-zhang/Fixup
https://github.com/hongyi-zhang/mixup
https://github.com/hooman650/supcl-seq
https://github.com/hope-yao/robust_attention_cifar
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/horse007666/ResNet
https://github.com/hosford42/EMNIST
https://github.com/hott-j/face-mask-detection
https://github.com/houstonsantos/CassavaLeafDisease
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hoyeoplee/pytorch-maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/hpnair/18663_Project_FBNet
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
https://github.com/hqhbench/hqhbench
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
https://github.com/hrdwsong/ProtoNet-Paddle
https://github.com/hrdwsong/TDLMR2AA-Paddle
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/hrshtv/pytorch-lmu
https://github.com/hs2k/pytorch-smoothgrad
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/hsalhab/Coloring-in-the-Deep
https://github.com/hsd1503/resnet1d
https://github.com/hsfzxjy/swavx
Deep Clustering for Unsupervised Learning of Visual Features
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.