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/0492wzl/tensorflow_slim_densenet
https://github.com/04RR/SOTA-Vision
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/0h-n0/tfdbonas
https://github.com/0h-n0/thdbonas
https://github.com/0h-n0/weight_normalization
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
https://github.com/0jason000/DenseNet
https://github.com/0jason000/GoogleNet
https://github.com/0jason000/Inception_V4
https://github.com/0jason000/MobileNet_V2
https://github.com/0jason000/MobilleNetV3
https://github.com/0jason000/S-GhostNet
https://github.com/0jason000/ShuffleNetV1
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/0jason000/convnext
https://github.com/0jason000/ghostnet
https://github.com/0jason000/inception_v3
https://github.com/0jason000/mae_vit
https://github.com/0jason000/wukong
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark
https://github.com/0xPr0xy/CoreML
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/0xPr0xy/MobileNet-CoreML
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/1044197988/TF.Keras-Commonly-used-models
https://github.com/10Exahertz/Text-ResNet-on-Sentimental-LIAR-Fake-News
https://github.com/1170500804/MoCo
Momentum Contrast for Unsupervised Visual Representation Learning
https://github.com/1170500804/tbmsl
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
https://github.com/1490581824/PyramidNet-Keras
https://github.com/163GitHub/AI
https://github.com/1Konny/FGSM
https://github.com/1M50RRY/resnet18-preact
https://github.com/1hb6s7t/PCGrad-mindspore-example
https://github.com/201419/Optimizer-PyTorch
https://github.com/2020132075/conmae
https://github.com/2023-MindSpore-1/ms-code-123
https://github.com/2023-MindSpore-1/ms-code-13
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/2023-MindSpore-1/ms-code-131
Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classification
https://github.com/2023-MindSpore-1/ms-code-14
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Pangu_alpha
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.