Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
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/geosada/pi
https://github.com/geox-lab/cmn
https://github.com/gfloto/tilted_prior
https://github.com/gfursin/browser-extension-for-reproducible-research
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/gfursin/ck-browser-addon
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/gfursin/ck-browser-addon-firefox
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/ghadahamed/darknet
https://github.com/ghaniskn/GCorp-Darknet
https://github.com/giakou4/pyfeats
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/giakou4/pyssl
Momentum Contrast for Unsupervised Visual Representation Learning
https://github.com/giannifranchi/LP_BNN
https://github.com/giannifranchi/NAO_morpho
https://github.com/giannifranchi/deeplabv3-superpixelmix
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/giantseaweed/decree
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/giarcieri/assessing-the-influence-of-models-on-the-performance-of-reinforcement-learning-algorithms
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
https://github.com/gietema/ood-early-layer-detection
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
https://github.com/gigantenbein/ReVAE
The Reversible Residual Network: Backpropagation Without Storing Activations
https://github.com/gigantenbein/UNet-Zoo
A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
https://github.com/gilesagnel/FGVC
https://github.com/gimme1dollar/vision-transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/gina-santana/American_Sign_Language
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/ginkyenglee/Explaining_Decision_of_Time_Series_Data
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
https://github.com/ginn24/RefineDet-1
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/giovanniguidi/deeplabV3-PyTorch
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/giovanniguidi/deeplabV3_Pytorch
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/giovannimaffei/concept_activation_vectors
https://github.com/github-jnauta/pytorch-pne
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/github-luffy/PFLD_68points_Pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/gitliber/image-colorization
https://github.com/gitlimlab/Representation-Learning-by-Learning-to-Count
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/gittar/tensorflow_transfer
https://github.com/gjacopo/poppysite
PlaNet - Photo Geolocation with Convolutional Neural Networks
https://github.com/glouppe/info8010-deep-learning
https://github.com/gmontamat/gentun
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
https://github.com/gmouzella/Efective_TensorFlow
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.