Papers with Code 아카이브
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/SyedUmaidAhmed/Pose-Detection-Sitting-Standing
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/SyogoShibuya/Chainer-Cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/TATlong/keras_bert
https://github.com/TArdelean/DynamicConvolution
https://github.com/TIBHannover/GeoEstimation
https://github.com/TJUdyk/CSWin-Transformer
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows
https://github.com/TOA-ZR/VATcode
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
https://github.com/TRAILab/ST-SLidR
https://github.com/TRKuan/cxr8
https://github.com/TTMRonald/MobileNet_Zoo
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/TUM-ML-Lab18/FaceSwap
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/TabishA/cgpunet
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
https://github.com/TahmasbiM/Example
https://github.com/TanyaChutani/DeepLabV3Plus-Tf2.0
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/TanyaChutani/Squeeze-and-Excitation-Networks-ResNet50-Tf2.0
https://github.com/TanyaChutani/Xception-Tf2.0
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/Taoudi/DataAugmentation
https://github.com/Taoudi/ImbalancedData
https://github.com/Taoudi/LotteryTicketHypothesis
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/TashinAhmed/BERT-Research
https://github.com/TashinAhmed/CNN_BERT
https://github.com/Tashnim-Chowdhury/FloodNet-Challenge-EARTHVISION2021
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/Techyee/darknet_resource
https://github.com/Tejas-Nanaware/GAN-Anime-Characters
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Tencent/TurboTransformers
https://github.com/Tennine2077/mindspore_Ghostnet
https://github.com/Tessii/Deep_Learning_Project_DTU
https://github.com/TetsumichiUmada/food_app
Food Image Recognition by Using Convolutional Neural Networks (CNNs)
https://github.com/Tetsuya-Nishikawa/ConvLSTM_DEMO
https://github.com/ThanasisMattas/smartflow
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/Thanusan19/Vision_Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/The-AI-Summer/self_attention
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
https://github.com/The-Shuai/Label-Embedded-Dictionary-Learning
https://github.com/TheConstant3/MobileNetV3-Keras
https://github.com/TheInfamousWayne/CheXNet
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.