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/ShubAn1901/Licence-Plate-Recognition
https://github.com/ShubAn1901/License-Plate-Recognition
https://github.com/Shubhambindal2017/Unoffical-Implementation-of-GRAD-CAM-with-Sanity-Checks
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Shubhammawa/MixMatch-Semi-Supervised-Learning
https://github.com/ShubhayanS/Multiclass-Diabetic-Retinopathy-Detection
Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset
https://github.com/ShuoYang-1998/Few_Shot_Distribution_Calibration
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
https://github.com/ShuoYang-1998/ICLR2021-Oral_Distribution_Calibration
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
https://github.com/SiHaoShen/Convolutional-Neural-Networks
https://github.com/SiavashCS/sgan_simple
https://github.com/SifatMd/Research-Papers
https://github.com/Silence-Monk/Silence-Monk-Papers-Online
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
https://github.com/Silver-L/OctConv
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/SimJeg/FC-DenseNet
https://github.com/SimRunBot/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/SimonThomine/DistillationAD
Student-Teacher Feature Pyramid Matching for Anomaly Detection
https://github.com/SimonThomine/RememberingNormality
Student-Teacher Feature Pyramid Matching for Anomaly Detection
https://github.com/SimonZsx/nasproj
https://github.com/SinclairHudson/DeepEquilibrium
https://github.com/SindhuMadi/FakeNewsDetection
https://github.com/SinghJasdeep/Projecting-Conflicting-Gradients
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Sivaneshmsc/FashionMNIST
https://github.com/SkyWa7ch3r/ImageSegmentation
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/SkyWa7ch3r/SceneSegmentation
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/SlongLiu/ASL_reproduce
https://github.com/Smiler-Jin/Yolov4
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/SnailDev/github-hot-hub
https://github.com/Soham0/Class2Str
https://github.com/SonalKumar95/IPCL
https://github.com/SonbolYb/Deep-Neuro-Fuzzy
https://github.com/SongweiGe/shift-invariance-adv-robustness
https://github.com/Songweiping/TCN-TF
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/SoojungYang/supervised_pretraining_GN_WS
https://github.com/Soonhwan-Kwon/capsnet.mxnet
https://github.com/Sooram/GradCam-tensorflow
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Sooram/cnn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.