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/Mind23-2/MindCode-122
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/Mind23-2/MindCode-123
https://github.com/Mind23-2/MindCode-155
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/Mind23-2/MindCode-165
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/Mind23-2/MindCode-168
https://github.com/Mind23-2/MindCode-176
https://github.com/Mind23-2/MindCode-18
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Mind23-2/MindCode-19
CoAtNet: Marrying Convolution and Attention for All Data Sizes
https://github.com/Mind23-2/MindCode-2
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/Mind23-2/MindCode-2/tree/main/vit_base
https://github.com/Mind23-2/MindCode-2/tree/main/wave_mlp
https://github.com/Mind23-2/MindCode-2/tree/main/wdsr
https://github.com/Mind23-2/MindCode-20
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
https://github.com/Mind23-2/MindCode-25
https://github.com/Mind23-2/MindCode-26
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/Mind23-2/MindCode-3
https://github.com/Mind23-2/MindCode-3/tree/main/HireMLP
https://github.com/Mind23-2/MindCode-3/tree/main/ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/Mind23-2/MindCode-3/tree/main/Inception-v2
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/Mind23-2/MindCode-3/tree/main/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Mind23-2/MindCode-3/tree/main/MIMO-UNet
Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression
https://github.com/Mind23-2/MindCode-3/tree/main/NFNet
https://github.com/Mind23-2/MindCode-3/tree/main/hlg
Vision Transformers: From Semantic Segmentation to Dense Prediction
https://github.com/Mind23-2/MindCode-3/tree/main/inception_resnet_v2
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/Mind23-2/MindCode-3/tree/main/lresnet100e_ir
https://github.com/Mind23-2/MindCode-3/tree/main/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/Mind23-2/MindCode-3/tree/main/mobilenetV3_small_x1_0
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/Mind23-2/MindCode-3/tree/main/mobilenetv3_large
https://github.com/Mind23-2/MindCode-3/tree/main/nasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/Mind23-2/MindCode-31
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-32
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Mind23-2/MindCode-37
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Mind23-2/MindCode-4
https://github.com/Mind23-2/MindCode-4/tree/main/resnet_ipl
https://github.com/Mind23-2/MindCode-4/tree/main/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.