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/anushkayadav/Denoising_cifar10
Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/aobaruwa/TensorFlow-Projects
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ap3885/street_view_imagery
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
https://github.com/apalle1/Sentiment-Span-Extraction-Using-Transformer-Models
Multi-Sample Dropout for Accelerated Training and Better Generalization
https://github.com/aparecidovieira/Keras_FusionNet
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
https://github.com/apeguero1/image-gpt/blob/master/Transformers_Image_GPT.ipynb
https://github.com/apllolulu/StereoConvNet
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/apoorvasrinivasan26/street_view_imagery
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
https://github.com/apple/ml-cvpr2019-swd
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
https://github.com/apple/ml-mobileclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/applecrumble123/SimCLR_pytorch
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/aragakiyuiii/Gumbel-darts-master
https://github.com/arahosu/musicslots
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/aravindMahadevan/metaLearningAlgos
https://github.com/ardeal/EfficientNetV2
https://github.com/areberoto/darknet-thesis
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/aredier/monte_carlo_dropout
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
https://github.com/areffarhadi/mamba-asr
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
https://github.com/ari-holtzman/genlm
https://github.com/ariG23498/TokenLearner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/ariG23498/involution-tf
Involution: Inverting the Inherence of Convolution for Visual Recognition
https://github.com/ariG23498/mae-scalable-vision-learners
https://github.com/ariangc/breinchallenge
https://github.com/ariesiitr/Image-Colourization
https://github.com/arindam-1991/i2-hofi
https://github.com/arkel23/Danbooru2018AnimeCharacterRecognitionDataset_Revamped
https://github.com/arkel23/PyTorch-Pretrained-ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/arm-software/ml-restructurable-activation-networks
Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge
https://github.com/armaank/archlectures
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/armavox/deepcoral-pchelkin
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
https://github.com/armin-azh/3DefficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/arnavc1712/DeepEnsembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/arnavdodiedo/DenseNet-Fashion-MNIST
https://github.com/arnavdodiedo/DenseNet-MNIST
https://github.com/arnomoonens/capsule-networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.