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/TheSaintIndiano/Bamboo-Forest-Detection
https://github.com/TheTensorDude/vision_transformer_tf
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Thehunk1206/Covid-19-chest-X-ray
https://github.com/Thehunk1206/Covid-19-covidcnn
https://github.com/ThiagoPiovesan/PeopleCounter_module
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/Thinklab-SJTU/CSL_RetinaNet_Tensorflow
https://github.com/ThmCuong/IIC-alter
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
https://github.com/ThomasEhling/Weight_Standardization
Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
https://github.com/ThomasGoerttler/similarity-analysis-of-maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Ti-Oluwanimi/Violence_Detection
SSIVD-Net: A Novel Salient Super Image Classification & Detection Technique for Weaponized Violence
https://github.com/TianheWu/LGPNet
https://github.com/TianyiZhang0315/CV_FinalProject
https://github.com/Tikquuss/meta_XLM
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/TillBeemelmanns/auto-augment-tf2-operations
https://github.com/TimoKuenstle/timeseries
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/Tingelam/tianchi_gd_defect_pytorch_round1
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/Tirth27/Skin-Cancer-Classification-using-Deep-Learning
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/Tnorm/SVM_cookietest
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
https://github.com/TobiasLee/Text-Classification
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/TomokiKomiya/SRGAN-keras
https://github.com/Tony-Y/pytorch_warmup
https://github.com/TonyYaoMSU/ProjectedGradientDescent
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/TooTouch/3D_CoordConv_Segmentation
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
https://github.com/TooTouch/VPT
https://github.com/TooTouch/WhiteBox-Part1
https://github.com/TooTouch/WhiteBox-Part2
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/Tools4Project/4501Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/TotalVariation/Flattenet
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/TowardHumanizedInteraction/TripletFace
https://github.com/TranNhiem/Neural_Network_Architecture_Search
$μ$NAS: Constrained Neural Architecture Search for Microcontrollers
https://github.com/TravisLeeTS/grabcvchallenge
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Trayan7/FixMatch-with-Soft-Labels
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/TreeLimes/QANAS
https://github.com/TrevorIkky/RegNet
https://github.com/TruongThaoNguyen/kakurenbo
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.