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/Candice-X/w-net-for-image-segmentation
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/CandiceD17/Bottleneck-Transformers-for-Visual-Recognition
https://github.com/CanshangD/ResNet_tensorflow2.0
https://github.com/CaoShuning/MIXUP
https://github.com/Carl0520/DANN_pytorch-
https://github.com/CarloLepelaars/filter_response_normalization_keras
https://github.com/CarloLepelaars/ghostnet_tf2
https://github.com/CeLaMbDa/DenseNet_keras
https://github.com/Celiali/FixMatch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/CellProfiling/HPA-Special-Prize
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
https://github.com/CellSMB/EM-net
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Celsuss/Residual_Attention_Network_for_Image_Classification
https://github.com/Chamchis/AICCTV
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/Chang-Chia-Chi/TrackNet-Badminton-Tracking-tensorflow2
https://github.com/ChangweiZhang/Awesome-WindowsML-ONNX-Models
https://github.com/Chaolei98/FreeZAD
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/CharlieSergeant/pollen
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/Chasing-After-AI/AI-papers
https://github.com/Cheng-Lin-Li/SegCaps
https://github.com/ChengBinJin/DCGAN-TensorFlow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ChengBinJin/WGAN-TensorFlow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ChengWeiGu/ResNeSt-Pytorch
https://github.com/Cherrydomini/Effects-of-feature-dropping-on-COMPAS-with-the-influence-of-LIME-
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/Chianugoogidi/X3D-tf
X3D: Expanding Architectures for Efficient Video Recognition
https://github.com/ChienliMa/DeConvNet
https://github.com/China-UK-ZSL/ZS-F-VQA
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
https://github.com/Ching-Chen-Wang/EfficientNet-eLite
https://github.com/ChingHuanChiu/sensitive
https://github.com/ChintanThacker/Facial_Expression_EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Chizuchizu/amplify-hackathon
https://github.com/ChriXiang/GloRe_pytorch
https://github.com/ChriXiang/Selective_Kernel_Keras
https://github.com/Chris-On-PC/darknet
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/ChrisAllenMing/Mixup_for_UDA
https://github.com/ChrisMats/Res2Net
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.