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/MindSpore-paper-code-3/code5/tree/main/res2net_deeplabv3
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet50_adv_pruning
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnetv2
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnetv2_50_frn
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnext152_64x4d
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet101
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet152
https://github.com/MindSpore-paper-code-3/code6/tree/main/Alexnet
https://github.com/MindSpore-paper-code-3/code6/tree/main/Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/MindSpore-paper-code-3/code6/tree/main/CBAM
https://github.com/MindSpore-paper-code-3/code6/tree/main/augvit
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
https://github.com/MindSpore-paper-code-3/code6/tree/main/autoaugment
https://github.com/MindSpore-paper-code-3/code6/tree/main/cait
https://github.com/MindSpore-paper-code-3/code6/tree/main/centernet_resnet101
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/MindSpore-paper-code-3/code6/tree/main/convnext
https://github.com/MindSpore-paper-code-3/code7/tree/main/FCANet
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code7/tree/main/fishnet99
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
https://github.com/MindSpore-paper-code-3/code7/tree/main/ghostnet_d
https://github.com/MindSpore-paper-code-3/code7/tree/main/ghostnet_quant
GhostShiftAddNet: More Features from Energy-Efficient Operations
https://github.com/MindSpore-paper-code-3/code8/tree/main/FCANet
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code8/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code8/tree/main/dcgan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MindSpore-paper-code-3/code8/tree/main/densenet
https://github.com/MindSpore-paper-code-3/code8/tree/main/dpn
https://github.com/MindSpore-paper-code-3/code8/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code9/tree/main/FasterRCNN
https://github.com/MindSpore-paper-code-3/code9/tree/main/Inception
https://github.com/MindSpore-paper-code-3/code9/tree/main/ShuffleNet
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/MindSpore-scientific-2/code-10/tree/main/Patient2Vec-A-Personalized-Interpretable
Knowledge distillation: A good teacher is patient and consistent
https://github.com/MindSpore-scientific-2/code-10/tree/main/SV-X-Softmax
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/MindSpore-scientific-2/code-10/tree/main/Translation-Invariant
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
https://github.com/MindSpore-scientific-2/code-10/tree/main/text-to-music
Music Artist Classification with Convolutional Recurrent Neural Networks
https://github.com/MindSpore-scientific-2/code-11/tree/main/S2Wrapper
A Framework For Contrastive Self-Supervised Learning And Designing A New Approach
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.