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/YeongHyeon/ReXNet-TF2
https://github.com/YeongHyeon/ResNeSt-TF2
https://github.com/YeongHyeon/SKNet-TF2
https://github.com/YeongHyeon/WideResNet_WRN-TF2
https://github.com/YerevaNN/DIIN-in-Keras
https://github.com/Yimin-Yuan/VeinProject
https://github.com/Yiming-M/CLIP-EBC
https://github.com/YinDFY/PGD_for_targeted_attack
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/YininKorea/Contour-aware-equipotential-learning
https://github.com/YirunKCL/Tensorflow-Keras-Involution2D
Involution: Inverting the Inherence of Convolution for Visual Recognition
https://github.com/YixuanLi/densenet-tensorflow
https://github.com/YizhuLiu/sumlen
https://github.com/Yochengliu/MLIC-KD-WSD
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
https://github.com/Yohei-Kawakami/Bouquet
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/Yonder-OSS/D3M-Primitives
https://github.com/YongWookHa/swin-transformer-ocr
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/YonghaoXu/RPNet
https://github.com/Youngb2n/GradCAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/YousefGamal220/Vision-Transformers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/YuHengsss/Trident
https://github.com/YuanTingHsieh/TF_TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/YudeWang/UNet-Satellite-Image-Segmentation
https://github.com/YufeiHU-fr/obow_ssl
OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning
https://github.com/Yuheon/Sharp-Aware-Minimization
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/Yui010206/CREMA
https://github.com/Yunodo/maxup
MaxUp: A Simple Way to Improve Generalization of Neural Network Training
https://github.com/YushiChen/Auto-CNN-HSI-Classification
https://github.com/YushiChen/Conv-Caps-HSI-Classification
https://github.com/YutongWangUMich/UCI_classification
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
https://github.com/Yuxiang1995/ICDAR2021_MFD
https://github.com/YuxiangZhang-BIT/IEEE_TNNLS_Gia-CFSL
https://github.com/Yuxin-Wen/OrthDNNs
https://github.com/Yuzhe-CHEN/NerfSNN
https://github.com/YvanG/VQGAN-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ZF4444/MMAL-Net
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.