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/cmasch/squeezenet
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/cmu-enyac/Renofeation
Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/cnichkawde/MatchingNetwork
https://github.com/cnielly/prototypical-networks-omniglot
https://github.com/coallaoh/whitenblackbox
https://github.com/cocopambag/ResNet
https://github.com/code-implementation1/Code1/tree/main/DeiT
Training data-efficient image transformers & distillation through attention
https://github.com/code-implementation1/Code1/tree/main/densenet
https://github.com/code-implementation1/Code1/tree/main/dpn
https://github.com/code-implementation1/Code2/tree/main/Alexnet
https://github.com/code-implementation1/Code3/tree/main/FaceAttribute
https://github.com/code-implementation1/Code3/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/code-implementation1/Code4/tree/main/HireMLP
https://github.com/code-implementation1/Code4/tree/main/ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/code-implementation1/Code4/tree/main/hlg
Vision Transformers: From Semantic Segmentation to Dense Prediction
https://github.com/code-implementation1/Code4/tree/main/inception_resnet_v2
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/code-implementation1/Code5/tree/main/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/code-implementation1/Code5/tree/main/MIMO-UNet
Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression
https://github.com/code-implementation1/Code5/tree/main/lresnet100e_ir
https://github.com/code-implementation1/Code5/tree/main/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/code-implementation1/Code6/tree/main/NFNet
https://github.com/code-implementation1/Code6/tree/main/PSPNet
https://github.com/code-implementation1/Code6/tree/main/ProtoNet
Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022
https://github.com/code-implementation1/Code6/tree/main/nasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/code-implementation1/Code6/tree/main/ntsnet
https://github.com/code-implementation1/Code6/tree/main/pnasnet
https://github.com/code-implementation1/Code6/tree/main/pointtransformer
https://github.com/code-implementation1/Code6/tree/main/proxylessnas
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/code-implementation1/Code7/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/code-implementation1/Code7/tree/main/RepVGG
https://github.com/code-implementation1/Code7/tree/main/ResNeXt
https://github.com/code-implementation1/Code7/tree/main/ResidualAttentionNet
https://github.com/code-implementation1/Code7/tree/main/SE_ResNeXt50
https://github.com/code-implementation1/Code7/tree/main/relationnet
https://github.com/code-implementation1/Code7/tree/main/res2net
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.